Clustering Algorithm in Manet

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.

A study of VMware ESXi 5.1 server has been carried out to find the optimal set of parameters which suggest usage of different resources of the server. Feature selection algorithms have been used to extract the optimum set of parameters of the data obtained from VMware ESXi 5.1 server using an esxtop command. Multiple virtual machines (VMs) are running in the mentioned server. K-means algorithm is used for
clustering the VMs. The goodness of each cluster is determined by Davies Bouldin index and Dunn index respectively. The best cluster is further identified by the determined indices. The features of the best cluster are considered into a set of optimal parameters.

Existing studies in data mining focus on Outlier detection on data with single clustering algorithm mostly. There are lots of Clustering methods available in data mining. The values or objects that are similar to each other are organized in group it’s called cluster and the values or objects that do not comply with the model or general behavior of the data these data objects called outliers. Outliers detect by clustering. Many Algorithms have been developed for clustering. Where partitional and Hierarchical Clustering is the two well known methods for clustering. In comparison of Hierarchical and Partitional Clustering Majority of the Hierarchical algorithms are very computationally, complex and consume high memory space. Whereas majority of Partitional clustering algorithm have required a linear time with better effectiveness. The clustering quality is not as Better as that a Hierarchical clustering algorithm. Hierarchical and Partitional Clustering algorithm have advantage over each other so in our proposed algorithm we integrate the Partitional Algorithm K-Modes because of Categorial Dataset and Hierarchical Clustering Algorithm CURE because of Large dataset, robust to outliers and identified cluster having non-spherical shape. And we plan to implement that algorithm in MapReduce Framework so the execution time of the algorithm is improve.

In recent years, many studies have been conducted in Mobile Ad Hoc Networks field in order to make a virtual infrastructure consisting of nodes. The common goal of all was to select a node called clusterhead which guarantees relationships between nodes. In this paper, we have presented a new clustering algorithm in Mobile Ad Hoc Network based on nodes weight. For calculating node weight we persent four new parameter, congestion, stability, number of nodes moving towards a node and battery remaining. The goal of this algorithm is to decrease the number of cluster forming, maintain stable clustering structure and maximize lifespan of mobile nodes in the system. In simulation, the proposed algorithm has been compared with WCA, MOBIC and the Lowest_ID algorithm. The results of simulation reveal that the proposed algorithm achieves the goals.

Mobile Ad hoc Networks have special properties and one of the most significant of those properties is nodes mobility which in part plays a considerable role in network's different parameters. Many efforts were made to make an infrastructure for these networks. In these infrastructures or clusters, a main node called clusterhead has a vital role in keeping the structure, routing and enhancing the efficiency of the network. In this paper, we have presented a new clustering algorithm in Mobile Ad Hoc Network based on nodes weight. For calculating node weight we present four new parameter, relative speed, stability, number of nodes moving towards a node and battery remaining. The goal of this algorithm is to decrease the number of cluster forming, maintain stable clustering structure and maximize lifespan of mobile nodes in the system. In simulation, the proposed algorithm has been compared with WCA, MOBIC and the Lowest_ID algorithm. The results of simulation reveal that the proposed algorithm achieves the goals.

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.