XML data clustering

Cloud computing is Internet-based computing that provides shared computer processing resources and data to computers and other devices on demand. Cloud computing is the hottest purpose built architecture created to support computer users. The cloud addresses three main areas of operation SaaS (software-as-a-service),PaaS (platform-as-a-service) and IaaS (infrastructure as a service). The large amount of data can be stored into cloud Data centers with low cost. We Integrate Data Mining and Cloud Computing to provide a quick access to this large volume amount of data on cloud. This paper aimed to study Clustering algorithm which can be applicable in cloud computing. This paper also describes the role of soft clustering in Cloud computing environment.

Health care industry produces enormous quantity of data that clutches complex information relating to patients and their medical conditions. Data mining is gaining popularity in different research arenas due to its infinite applications and methodologies to mine the information in correct manner. Data mining techniques have the capabilities to discover hidden patterns or relationships among the objects in the medical data. In last decade, there has been increase in usage of data mining techniques on medical data for determining useful trends or patterns that are used in analysis and decision making. Data mining has an infinite potential to utilize healthcare data more efficiently and effectually to predict different kind of disease. This paper features various Data Mining techniques such as classification, clustering, association and also highlights related work to analyse and predict human disease.

Electricity may not be economically stored as other forms of energy such that it would be in short supply during the peak time. In view of this difficulty, most power suppliers encourage their customers to adopt time-of-use rate plans. Consequently, it is essential for a user to be able to perceive the real-time information of power consumption. With the advancement of Internet of Things technologies, smart sockets are becoming a commodity to manage power consumption in a household. However, current smart sockets merely present the total electricity consumption rather than the individual consumption of household appliances. In this work, we thus design the capability of appliance recognition and implement this feature into a smart socket so that we can identify the power consumption of each appliance respectively. The proposed recursive DBSCAN approach realizes the recognition task without prior knowledge of new appliances and shows a feasible result in our experimental studies.

— In searching process user enter particular candidate searching keyword and with the help of searching algorithm respective searching query is executed on targeted dataset and result is return as an output of that algorithm. In this case it is expected that meaningful keyword has to be entered by user to get appropriate result set. In case of confusing bunch of keywords or ambiguity in it or short and indistinctness in it causes an irrelevant searching result. Also searching algorithms works on exact result fetching which can be irrelevant in case problem in input query and keyword. This problem statement is focused in this system. By considering the keyword and its relevant context in XML data , searching should be done using automatically diversification process of XML keyword search. In this way system may satisfy user, as user gets the analytical result set based on context of searching keywords. For more efficiency and to deal with big data, HADOOP platform is used. baseline efficient algorithms are proposed to incrementally compute top-k qualified query candidates as the diversified search intentions. Compare selection criteria are targeted: the k selected query candidates are most relevant to the given query while they have to cover maximal number of distinct results on real and synthetic data sets demonstrates the effectiveness diversification model and the efficiency of algorithms

The ubiquitous nature of XML as a data interchange format and the arrival of XMLdatabases offers new opportunities and challenges to data mining. The XML format permits the
representation of data structures that cannot be easily mapped into a relational framework. New opportunities exist to mine relationships expressed by tree position and the presence or absence of sub trees as well as the conventional categorical and numeric data values contained at leaf nodes. Thispaper will consider why most conventional data mining algorithms are ill suited to mining tree data,
describe a novel XML based knowledge representation methodology called Metarule, and describe algorithms that do perform well. Two examples drawn from business will be used to demonstrate the application of these novel techniques.
Keywords: Data-mining, XML, Unstructured data

In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks (MANETs) is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can operate without using focal access points, pre-existing infrastructures, or a centralized management point. In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead to various problems in the routing process such as increase of the overhead massages and inefficient routing between nodes of network. A large variety of clustering methods have been developed for establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having significant impact on MANETs performance. The K-means algorithm is one of the effective clustering methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption. This paper proposed a new K-means clustering algorithm to find out optimal path from source node to destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the performance of routing process in Mobile ad-hoc networks.

Decision making both on individual and organizational level is always accompanied by the search of other’s opinion on the same. With tremendous establishment of opinion rich resources like, reviews, forum discussions, blogs, micro-blogs, Twitter etc provide a rich anthology of sentiments. This user generated content can serve as a benefaction to market if the semantic orientations are deliberated. Opinion mining and sentiment analysis are the formalization for studying and construing opinions and sentiments. The digital ecosystem has itself paved way for use of huge volume of opinionated data recorded. This paper is an attempt to review and evaluate the various techniques used for opinion and sentiment analysis.