Association Rules Mining

Data is raw facts and figures and information is meaningful data that would be helpful for a person or company. Business intelligence extracts information from raw data through tools like data mining, perspective analysis, online analytical processing etc. Business Intelligence (BI) can be used to support a wide range of business decisions ranging from operational to strategic.
The textbook will provide comprehensive information to readers about business intelligence and analytics.

The intent of this chapter is to re-imagine and apply the term " civil disobedience " to the cyber domain. The full extent of how it can apply will be analyzed in a framework of the evolutionary rights of protest. Traditional civil disobedience is a " performance " via visual and physical mediums, while cyber disobedience is essentially a digital performance with difficulty in attributing true ownership of the act. This is an interesting antithesis to the physical protestor, who uses strategic visibility, threat of arrest, and popular backlash against state behavior as vital aspects of the disobedience. These variables are noticeably absent in cyber disobedience.

Market-Basket Analysis is a process to analyse the habits of buyers to find the relationship between different items in their market basket. The discovery of these relationships can help the merchant to develop a sales strategy by considering the items frequently purchased together by customers. In this research, the data mining with market basket analysis method is implemented, where it can analyse the buying habit of the customers. The testing is conducted in Minimarket X. Searching for frequent item sets performed by Apriori algorithm to get the items that often appear in the database and the pair of items in one transaction. Pair of items that exceed the minimum support will be included
into the frequent item sets are selected. Frequent item sets that exceed the minimum support
will generate association rules after decoding. One frequent item sets can generate association
rules and find the confidence. The test results show, the application can generate the
information what kind of products are frequently bought in the same time by the customers
according to Hybrid-dimension Association Rules criteria. Results from the mining process
show a correlation between the data (association rules) including the support and confidence
that can be analysed. This information will give additional consideration for owners of
Minimarket X to make the further decision.

With the e-commerce applications growing rapidly, the companies have a significant amount of data in
their hands. Data Mining is one of the methods for extracting useful information from this raw data.
The aim of Data Mining is to find relations in the data.
Mining Association Rules (Market Basket Analysis) is one of the main application areas of Data
Mining. Given a set of customer transactions on items, the aim is to find correlations between the
sales of items.
We applied Market Basket Analysis to some subset of the data taken from some stores of Gima Türk
A.Ş. and we obtained promising results.

Text mining is the process of extracting interesting and non-trivial knowledge or information from unstructured text data. Text mining is the multidisciplinary field which draws on data mining, machine learning, information retrieval, computational linguistics and statistics. Important text mining processes are information extraction, information retrieval, natural language processing, text classification, content analysis and text clustering. All these processes are required to complete the preprocessing step before doing their intended task. Pre-processing significantly reduces the size of the input text documents and the actions involved in this step are sentence boundary determination, natural language specific stop-word elimination, tokenization and stemming. Among this, the most essential and important action is the tokenization. Tokenization helps to divide the textual information into individual words. For performing tokenization process, there are many open source tools are available. The main objective of this work is to analyze the performance of the seven open source tokenization tools. For this comparative analysis, we have taken Nlpdotnet Tokenizer, Mila Tokenizer, NLTK Word Tokenize, TextBlob Word Tokenize, MBSP Word Tokenize, Pattern Word Tokenize and Word Tokenization with Python NLTK. Based on the results, we observed that the Nlpdotnet Tokenizer tool performance is better than other tools.

Abstrak – Product bundling atau paket produk yang sering dijumpai di swalayan atau mini-market. Hal ini dapat meningkatkan keuntungan penjualan baik dari segi finansial maupun brand produk tersebut. Data mining atau penggalian data adalah cara dalam mengetahui sejumlah pola informasi pada suatu data yang besar dan bermanfaat bila bersifat implisit dan belum diketahui sebelumnya. FP-Growth adalah satu algoritma alternatif yang dapat digunakan untuk menentukan himpunan data yang sering muncul (frequent itemset) dalam sebuah kumpulan data. Penelitian ini akan membahas proses pembentukan FP-tree dari sekumpulan data transaksi untuk menemukan pola pembelian produk oleh pelanggan dan membentuknya menjadi saran untuk product bundling. Dengan nilai support sebesar 20% dan confidence sebesar 60% sebagai parameter optimal untuk algoritma FP-Growth dan Apriori, hasil algoritma FP-Growth menghasilkan bundling yang lebih banyak dan performa yang lebih cepat dibanding algoritma Apriori.

The aim of this paper is to find out the patterns of incidents in a steel plant in India. Occupational incidents occur in steel plant mainly in form of injury, near miss, and property damage or in combination. Different factors are responsible for such incidents to occur. An incident investigation scheme is proposed. Association rule mining approach is used to discover cause-and-affect patterns (rules) using 843 incidents. Thirty-five meaningful association rules are extracted using three criteria, support (S), confidence (C) and lift (L). For example, the results show that unsafe acts done by others are more frequent in injury cases (S = 4.86%, C = 78.8%, L = 2.3). Similarly, one of the SOP (standard operating procedures) related rule: ‘SOP required, available, adequate but not complied’ led to property damage (S = 11.03%, C = 49.2%, L = 1.525). Another useful rule ‘SOP required, available but inadequate, followed’ led to near miss (S = 1.66%, C = 38.89%, L = 1.163). It is also found that for slip, trip and fall incidents, workers working alone (S = 3.91%, C = 76.74%, L = 2.239) or in a group (S = 3.20%, C = 75.00%, L = 2.188) does not make much difference. The findings pinpoint the areas of improvement such as inadequate SOPs, non-compliance of SOPs, training, and slip, trip and fall prevention to minimize incidents.

Strong rule generation is an important area of data mining. In this paper we design a novel method for generation of strong rule. In which a general Apriori algorithm is used to generate the rules after that we use the optimization techniques. Genetic algorithm is one of the best ways to optimize the rules .In this direction for the optimization of the rule set we design a new fitness function that uses the concept of supervised learning then the GA will be able to generate the stronger rule set.

In mining operations carried out below the water table, mine area could potentially affect the surrounding. With further deepening of the mine and quarry, the drawdown can impact on water supply wells and base flow. The variation in radius of influence under confined and unconfined aquifer condition is assessed using a hypothetical case study with three operational quarry sites. Average pit-wise radius of influence (R0) for the pit mine, under unconfined and confined aquifer conditions is 963 m and 61 km, respectively, whereas effective radius (Re) for total (cumulative) excavated mine area is 1.7 and 146 km, respectively. It means that the maximum and minimum value of overall impact/influence for the unconfined aquifer lies in-between 963 m and 1.7 km. Similarly that the maximum and minimum value of overall impact/influence for the confined aquifer lies in-between 61 and 146 km. Meanwhile, Re or effective radius for total (cumulative) excavated area seems more appropriate from evaluation viewpoint due to the overlapping (superposition) concentric mining quarries. Thus, assessment of quarry-wise and total radius of influence is of importance to fulfil targeted production, economically with minimum interruptions as part of the mine planning. Moreover, it helps us to assess, monitor and regulate the impact of a mine-dewatering program in the area. Also, the pumping test is often used to estimate radius of influence; it can trigger the inflow or radius of influence. Estimate of the radius of influence using analytical equation mainly independent of discharge/abstraction from the aquifer stands as a preferred choice for the prediction of radius of influence at the quarry/mine pit. There is a strong rationale that policies should be informed by such analytical assessment because it helps us to prevent or minimize negative impacts as part of regulation and pit-mining management and are useful tools for practitioners to design economically efficient and cost-effective in situ groundwater remediation systems, to contain contaminant plumes, to evaluate the surface–subsurface water interaction and to verify numerical models.

It is widely known that a construction project can be described as a success if it is completed on time and without cost overrun. As the strong correlation between delay in completion date of construction projects and cost overruns exists, it is necessary to manage the risk of a delay from the very beginning: even before the start of works execution. The data concerning 139 construction contracts of the highway sections already completed, as well as their completion dates, were collected. The twelve types of data were chosen based on the criterion of time of appearance: they should be known before the start of construction works. Most of the contracts' properties have continuous values, and these had to be converted to the binary values. With the use of the association analysis, it is possible to find the combination of properties of the construction contracts that have produced significant delays in completion date in large part of analysed and already completed contracts. For this purpose, appropriate software has been created. This paper will comprise the software approach to the problem as well as the result of the analysis, as it is based on real data completed. Getting the information on which combination of construction contract properties can cause a significant delay in completion date of the contract, the client can make decisions leading to avoiding risk, i.e. providing better circumstances for project success.

Flight delays hurt airlines, airports, and passengers. Their prediction is crucial during the decision making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data. In this context, this paper presents a thorough literature review of approaches used to build flight delay prediction models from the Data Science perspective. We propose a taxonomy and summarize the initiatives used to address the flight delay prediction problem, according to scope, data, and computational methods, giving particular attention to an increased usage of machine learning methods. Besides, we also present a timeline of significant works that depicts relationships between flight delay prediction problems and research trends to address them.

The published version of this paper is made available at https://doi.org/10.1080/01441647.2020.1861123.
Please cite as:
L. Carvalho, A. Sternberg, L. Maia Gonc¸alves, A. Beatriz Cruz, J.A. Soares, D. Brand˜ao, D. Carvalho, e E. Ogasawara, 2020, On the relevance of data science for flight delay research: a systematic review, Transport Reviews

More and more devices featuring internet connectivity are being created every day. The value of such devices is in their ability to interact with one another, as part of automation workflows created by users to improve their home experience. Various platforms offer systems that allow for the creation of automation rules between different connected devices, but formulating and defining such automations can be quite complex.
This work explores the automated, personalized recommendation of automation rules for users of connected devices. The application of recommender systems’ techniques to this domain is novel,
so that exploring the applicability of the recommendation approaches discussed in the literature is one of the main contributions of this work. The hypothesis that certain groups of automations provide synergies was explored, and a system that exploits this by providing recommendations based on learning association rules was developed. Various strategies to make association rule mining feasible
on an automation dataset were devised, such as generalizing automation rules into recommendable items, and applying a similarity operator to all items, making automations identifiable across users.
The developed system showed very promising results, under an evaluation methodology consisting of gathering precision, recall and coverage metrics for the most important hyperparameters, as well as when comparing it against a naive recommender developed as a baseline. Finally, it was discussed how the techniques developed in this work are generalizable to domains outside of automation rule territory.

In recent times, customer behaviour models are typically based on data mining of customer data, and each model is designed to answer one question at one point in time. Predicting customer behaviour is an uncertain and difficult task. Thus, developing customer behaviour models requires the right technique and approach. Once a prediction model has been built, it is difficult to manipulate it for the purposes of the marketer, so as to determine exactly what marketing actions to take for each customer or group of customers. Despite the complexity of this formulation, most customer models are actually relatively simple. Because of this necessity, most customer behaviour models ignore so many pertinent factors that the predictions they generate are generally not very reliable. This paper aims to develop an association rule mining model to predict customer behaviour using a typical online retail store for data collection and extract important trends from the customer behaviour data.

A development for building an application of data mining algorithms requires the use of powerful software and programming tools. As the number of available tools continues to grow, the choice of the most prominent and suitable tool becomes increasingly difficult. This paper attempts to support the decision-making process in choosing the best tools by discussing the historical development and presenting the existing open source and state-of-the-art data mining tools. The data mining tools that will be presented here are WEKA (Waikato Environment for Knowledge Analysis) and RapidMiner (RM). These tools are among the top 5 popular DM tools and also commercial with free licenses for academic purpose and use. Furthermore, we propose criteria for the tool categorization based on different user groups, data structures, data mining tasks and methods, visualization and interaction styles, import and export options for data and models, platforms, and license policies. These criteria are then used to classify data mining tools into nine different types. The typical characteristics of these types are explained and a selection of the most important tools is categorized.

Akumul asi data yang saat ini sangat cepat dan signifikan, hanya saja persoalan yang diperoleh adalah karena kuranganya informasi yang digunakan dalam pengolahannya. Data mining adalah sebuah proses penggalian data dan mengolahnya dengan metode-metode tertentu yang akan digunakan untuk mengolah data yang besar agar memperoleh sebuah hasil yang optimah atau diinginkan. Penggalian data akan dimulai dari sample peminjaman buku perpustakaan yang saling berkaitan satu sama lain sehingga akan memperoleh hasil sebuah penyusunan buku yang sesuai dengan tingkat support dan confidence.
Keyword : data mining, association rule, support and confidence.

The management and organization of archaeological data in complex database management systems (DBMS), and more recently data warehouses, such as the Online Cultural and Historical Research Environment (OCHRE), has become commonplace within the discipline. Traditionally, Near Eastern archaeologists have emphasized data interoperability and integration for inter-site comparison
of archaeological data, often through attempts to establish data “standards,” which has proven relatively unfruitful. Furthermore, as highly atomized, item-based data structures come to replace the traditional relational data model often utilized by archaeologists, it has become apparent that a data standard is not necessary as long as our data ontologies remain appropriately abstract (i.e., highly itemized). In doing so, we can conceive of DBMS not as simple repositories of data, but as analytical environments in their own right, wherein lies the potential for not only the accumulation, but the construction and contestation of knowledge from archaeological data (Knowledge Discovery from Archaeological Data [KDAD]). Following a brief excursus into the phenomenon of data mining and its implications within the discipline, this paper will focus on the implementation of a revised apriori algorithm (after Agrawal and Srikant 1994) for establishing associations among items in a dataset. The algorithm is applied to a dataset from Tell Mastuma, Syria, to elucidate heretofore unrecognized patterns among the data derived from Stratum I-2b. It is argued such patterns are indicative of past practices enacted at the site, and association rules are utilized to paint a more nuanced portrait of the community of Tell Mastuma during the Iron II.

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant associations between services provided by devices and embedded agents residing in Ambient Intelligent Environments (AIEs). The embedded agents perform two processes where the first process monitors the inhabitants of the AIE and learns their behaviors in an online, non-intrusive and life-long fashion. The second process then evaluates the relevance and significance of the associations to various services and eliminates the redundant associations in order to minimize the agent computational latency within the AIE. We will present real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture.

—Pervasive Computing is one of the latest and more advanced paradigms currently available in the computers arena. Its ability to provide the distribution of computational services within environments where people live, work or socialize leads to make issues such as privacy, trust and identity more challenging compared to traditional computing environments. In this work we review these general issues and propose a Pervasive Computing architecture based on a simple but effective trust model that is better able to cope with them. The proposed architecture combines some Artificial Intelligence techniques to achieve close resemblance with human-like decision making. Accordingly, Apriori algorithm is first used in order to extract the behavioral patterns adopted from the users during their network interactions. Naïve Bayes classifier is then used for final decision making expressed in term of probability of user trustworthiness. To validate our approach we applied it to some typical ubiquitous computing scenarios. The obtained results demonstrated the usefulness of such approach and the competiveness against other existing ones.

The Apriori algorithm that mines frequent itemsets is one of the most popular and widely used data mining algorithms. Now days many algorithms have been proposed on parallel and distributed platforms to enhance the performance of Apriori algorithm. They differ from each other on the basis of load balancing technique, memory system, data decomposition technique and data layout used to implement them. The problems with most of the distributed framework are overheads of managing distributed system and lack of high level parallel programming language. Also with grid computing there is always potential chances of node failures which cause multiple re-executions of tasks. These problems can be overcome by the MapReduce framework introduced by Google. MapReduce is an efficient, scalable and simplified programming model for large scale distributed data processing on a large cluster of commodity computers and also used in cloud computing. In this paper, we present the overview of parallel Apriori algorithm implemented on MapReduce framework. They are categorized on the basis of Map and Reduce functions used to implement them e.g. 1-phase vs. k-phase, I/O of Mapper, Combiner and Reducer, using functionality of Combiner
inside Mapper etc. This survey discusses and analyzes the various implementations of Apriori on MapReduce framework on the basis of their distinguishing characteristics. Moreover, it also includes the advantages and limitations of MapReduce framework.

Most of the established companies have accumulated masses of data from their
customers for decades. With the e-commerce applications growing rapidly, the companies
will have a significant amount of data in months not in years. Data Mining,
also known as Knowledge Discovery in Databases (KDD), is to find trends, patterns,
correlations, anomalies in these databases which can help us to make accurate future
decisions.
Mining Association Rules is one of the main application areas of Data Mining.
Given a set of customer transactions on items, the aim is to find correlations between
the sales of items.
We consider Association Mining in large database of customer transactions. We
give an overview of the problem and explain approaches that have been used to attack
this problem. We then give the description of the Apriori Algorithm and show results
that are taken from Gima Türk A.Ş. a large Turkish supermarket chain. We also
use two statistical methods: Principal Component Analysis and k-means to detect
correlations between sets of items.

Association Rule Mining (ARM) is mined valuable information from large voluminous databases. But most of ARM algorithms are mined many uninteresting or unrelated knowledge. We proposed new Association rule mining from data with missing values (ARDM) algorithm to mine domain irrelevant rules. Acquired Immuno Deficiency Syndrome (AIDS) is a challenge infection in the field of medical domain. The paper proposes a new algorithm for dealing with AIDS patients' case history. The data were collected from Midwest clinics, London. The ARDM algorithm applies with the proposed dataset. It mines many interesting rules, provides much useful information to domain experts. The proposed ARDM algorithm is performed better than Robust association rule mining (RAR) algorithm.

Background: The prevalence of obesity remains a major public health concern and there has been a significant increase in childhood obesity in the USA. Aim: This study seeks to uncover the major patterns of dietary habits in relation to physical activity, together with students' opinions about the quality of food in the school's cafeteria and vending machines. Methods: The empirical work of this study is based on the 2011 Healthy School Program (HSP) Evaluation. HSP assesses the demographic characteristics as well as the dietary habits and exercise patterns of a representative sample of elementary, middle, and high school students in the USA. Results: Findings suggest that students assigned to different clusters have different eating habits, exercise patterns, weight status, weight management, and opinions about the quality of food in the school's cafeteria and vending machines. There is great variation in dietary profiles and lifestyle behaviors among students who identified themselves as either overweight or unsure about their weight status. Conclusions: Findings from this study may inform future interventions regarding how to promote student's healthy food choices when they are still in school. Health promotion initiatives should specifically target students with persistently unhealthier dietary profiles.

This paper presents a novel fuzzy-based intelligent architecture that aims to find relevant and important associations between embedded-agent based services that form Ambient Intelligent Environments (AIEs). The embedded agents are used in two ways; first they monitor the inhabitants of the AIE, learning their behaviours in an online, non-intrusive and life-long fashion with the aim of pre-emptively setting the environment to the users preferred state. Secondly, they evaluate the relevance and significance of the associations to various services with the aim of eliminating redundant associations in order to minimize the agent computational latency within the AIE. The embedded agents employ fuzzy-logic due to its robustness to the uncertainties, noise and imprecision encountered in AIEs. We describe unique real world experiments that were conducted in the Essex intelligent Dormitory (iDorm) to evaluate and validate the significance of the proposed architecture and methods.

Abstract - Mining of multiple level association rules is a process of data mining, in which significant implication relationship of useful items can be extracted in the form of knowledge at different levels of abstraction. It determines interesting relations between data items through various levels. Association rules with multiple levels of abstraction are more practical and superior as compare to single level association rules. Numerous algorithms had been proposed by the researchers for finding multilevel association rules. The majority of the presented algorithms in this direction relied upon Apriori, and FP-growth, which are based on tire-out explore methods and face problems with large databases. To facilitate multilevel association rules searching, from large databases, a novel algorithm named as “MLTransTrie” is proposed in this paper which is based on bottom-up approach. This newly developed algorithm helps in reducing number of iterations of database as well as it takes less space in computer memory due to transposed representation of database.

Keywords- Frequent Itemsets; Minimum Support; Multiple Level Association Rule; Transposed Database; Trie.

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ISSN 1947-5500

The vacancy stock of one product that happen when the customers are purchasing more than one item at the same time is as a result from lacking information of the customers shopping habit. Therefore, information extraction on data transaction through Association Rule Mining technique is necessarily needed in order to find out what products are usually bought at the same time by the customers. Association rule mining is also called the Market Basket Analysis. Algorithms which is used in Association Rule Mining in this study is Apriori Algorithm. The products combination is based on minimum support value, minimum confidence value, and sales transaction data range. One of the resulting information is if the customers buy 25 kilograms of Cakra Kembar then they would buy 25 kilograms of Segitiga Biru with support value 11.76 % and confidence value 43.24 %. Based on some test results, it can be seen that the minimum support value affected the number of frequent item set which is generated. The minimum support value and minimum confidence value affected the time process of system in the information extraction.
Sering terjadinya kekosongan salah satu stok barang yang sering dibeli secara bersamaan oleh pelanggan merupakan akibat dari tidak adanya informasi mengenai kebiasaan belanja pelanggan. Sehingga perlu dilakukan penggalian informasi pada data transaksi dengan teknik association rule mining (aturan asosiasi) untuk mengetahui barang yang sering dibeli secara bersamaan oleh pelanggan. association rule mining juga sering dinamakan market basket analysis (analisis keranjang belanja). Algoritma yang digunakan dalam association rule mining pada penelitian ini adalah algoritma apriori. Kombinasi barang dicari berdasarkan nilai minimum support, nilai minimum confidence, dan rentang data transaksi penjualan yang dimasukan. Salah satu informasi yang dihasilkan adalah Jika membeli Cakra Kembar 25 Kg maka akan membeli Segitiga Biru 25 Kg dengan nilai support 11,76 % dan nilai confidence 43,24 %. Berdasarkan beberapa hasil uji coba dapat diketahui bahwa nilai minimum support yang dimasukan mempengaruhi banyak frequent itemset yang dihasilkan. Nilai minimum support dan minimum confidence yang dimasukan mempengaruhi waktu proses sistem dalam penggalian informasi.

With the growing data available on the Internet, customization of the web sites information has become a requirement for users. A procedure for the appropriate customization of web data is configured by automatic extraction of combined knowledge of the log file and user profile information. In this paper, integrating decision tree and association rules for user profile information and log information of website in an online shopping store is targeted. The tangible results of such a framework for decision makers and marketers are customization of web pages and statistical analysis for sale improvement. Applying association rules, the website users' patterns are mined and utilizing decision tree users are classified and their interests are determined. By combining the results of two algorithms and its analysis, the behavior models from user profile, user interests in terms of age and gender, and the most visited web pages by subject can be achieved.

Our environments are being gradually occupied with an abundant number of digital objects with networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by a user or other networked device. With the increasing number of these devices attached to networks the complexity to configure and control them increases which may lead to major processing and communication overheads. Hence, the devices are no longer expected to just act as primitive stand-alone appliances which only provide the facilities and services to the user they are designed for, rather they can offer complex services from unique combinations of devices; which in turn creates the necessity for these devices to be equipped with some sort of intelligence and selfawareness which enables them to be self configuring and self programming. Intelligence in devices is obtained by embedding intelligent agents into them which provides them with proactive control and learning capabilities. Selfawareness within agents enables capabilities to operate in with a minimum of cognitive loading of the user, thereby supporting the vision for cognitive disappearance or ambient intelligence. This paper presents a novel intelligent embedded agent technique for reducing the number of associations and interconnections between various agents operating within an AIE in order to minimize the processing latency and overhead caused by message flooding in a Publish/Subscribe (pub/sub) middleware whilst reducing the cognitive load of configuring these associations to personalize themselves to the user needs. The main goal of the proposed fuzzy based intelligent embedded agents (F-IAS) includes learning and adapting the network configuration and the system functionality to meet the user’s needs based on monitoring the user behaviors in a lifelong non intrusive mode to preemptively control the environment on his behalf. In addition, the F-IAS agent aimed at reducing the agent associations and interconnections to the most relevant set in order to reduce its processing overheads and thus implicitly improving the system overall efficiency. Moreover, we employ embedded ambassador agents, namely embassadors, which limit the number of messages reaching the societies by performing an analysis and filtering routine to determine if the propagated events match the desired criteria of the member agents of the societies. Embassadors are also utilized with novel characteristics to discover and select associations among agent pairs residing in separate societies based on a concurrence analysis of published events. In order to validate the efficiency of the proposed methods we will present two set of unique experiments. The first experiments described the obtained results carried out within the intelligent Dormitory (iDorm) which is a real world test bed for AIE research. Here we specifically demonstrate the utilization of the F-IAS agents and discuss that by optimizing the set of associations, the agents increases efficiency and performance. The second set of experiments is based on emulation of an iDorm-like large scale multi society based AIE environment. The results illustrate how embassadors discover strongly correlated agent pairs and

This paper formulates the problem of a rule-based machine learning method to discover the behavioral rules of individual smartphone users to provide context-aware intelligent services. Smartphones nowadays are considered as one of the most important Internet-of-Things (IoT) devices for providing various context-aware personalized services. These devices can record individuals' contextual data-for example, temporal, spatial, or social contexts, and their daily behavioral activity records. Association rule mining (ARM) is the most popular rule-based machine learning method for discovering rules for a particular constraint preference utilizing a given dataset. However, it generates numerous uninteresting contextual associations which lead to generate huge number of redundant rules that become useless in making context-aware decisions. This redundant generation makes not only the rule-set unnecessarily large but also makes the context-aware decision making process more complex and ineffective. To minimize these issues, in this paper, we propose a rule-based machine learning method "ABC-RuleMiner" that effectively identifies the redundancy in associations and discovers a set of non-redundant behavioral rules (IF-THEN) by taking into account the precedence of relevant contexts. Our experiments on individuals' contextual smartphone datasets show that this rule discovery approach is more effective while comparing with traditional rule-based methods.

Mining frequent itemsets from massive datasets is always being a most important problem of data mining. Apriori is the most popular and simplest algorithm for frequent itemset mining. To enhance the efficiency and scalability of Apriori, a number of algorithms have been proposed addressing the design of efficient data structures, minimizing database scan and parallel and distributed processing. MapReduce is the emerging parallel and distributed technology to process big datasets on Hadoop Cluster. To mine big datasets it is essential to re-design the data mining algorithm on this new paradigm. In this paper, we implement three variations of Apriori algorithm using data structures hash tree, trie and hash table trie i.e. trie with hash technique on MapReduce paradigm. We emphasize and investigate the significance of these three data structures for Apriori algorithm on Hadoop cluster, which has not been given attention yet. Experiments are carried out on both real life and synthetic datasets which shows that hash table trie data structures performs far better than trie and hash tree in terms of execution time. Moreover the performance in case of hash tree becomes worst.

Data mining is a new concept & an exploration and analysis of large data sets, in order to discover meaningful patterns and rules. Many organizations are now using the data mining techniques to find out meaningful patterns from the database. The present paper studies how data mining techniques can be apply to the large database. These data mining techniques give certain behavioral pattern from the database. The results which come after analysis of the database are useful for organization. This paper examines the result after applying association rule mining technique, rule induction technique and Apriori algorithm. These techniques are applied to the database of shopping mall. Market basket analysis is performing by the above mentioned techniques and some important results are found such as buying behavior.

Data mining techniques are used for variety of applications. In health care industry, data mining plays an important role for predicting diseases. For detecting a disease number of tests should be required from the patient. Data mining is defined as shifting through very large amounts of data for useful information. Some of the most important and popular data mining techniques are association rules, classification, clustering, prediction and sequential patterns. But using data mining technique the number of test should be reduced. This reduced test plays an important role in time and performance. This technique has an advantages and disadvantages. This research paper analyzes how data mining techniques are used for predicting different types of major life threatening diseases.

This paper represents comparative evaluation of different type of algorithms for association rule mining that works on frequent item sets. Association rule mining between different items in large-scale database is an important data mining problem. Now a day there is lots of algorithms available for association rule mining. To perform comparative study of different algorithms various factor considered in this paper like number of transaction, minimum support and execution time. Comparisons of algorithms are generated based on experimental data which gives final conclusion.

A high monetary value of the construction projects is one of the reasons of frequent disputes between a general contractor (GC) and a client. A construction site is a unique, one-time, and singleproduct factory with many parties involved and dependent on each other. The organizational dependencies and their complexity make any fault or mistake propagate and influence the final
result (delays, cost overruns). The constant will of the parties involved results in completing a construction object. The cost increase, over the expected level, may cause settlements between parties difficult and lead to disputes that often finish in a court. Such decision of taking a client to a court may influence the future relations with a client, the trademark of the GC, as well as, its finance. To ascertain the correctness of the decision of this kind, the machine learning tools as decision trees
(DT) and artificial neural networks (ANN) are applied to predict the result of a dispute. The dataset of about 10 projects completed by an undisclosed contractor is analyzed. Based on that, a much bigger database is simulated for automated classifications onto the following two classes: a dispute won or lost. The accuracy of over 93% is achieved, and the reasoning based on results from DT and ANN is presented and analyzed. The novelty of the article is the usage of in-company data as the independent variables what makes the model tailored for a specific GC. Secondly, the calculation of the risk of wrong decisions based on machine learning tools predictions is introduced and discussed.