A SimRank based Ensemble Method for Resolving Challenges of Partition Clustering Methods

323–327Traditional clustering techniques alone cannot resolve all challenges of partition-based clustering methods. In the partition based clustering, particularly in variants of K-means, initial cluster centre selection is a significant and crucial point. The dependency of final cluster is totally based on initial cluster centres; hence, this process is delineated to be most significant in the entire clustering operation. The random selection of initial cluster centres is unstable, since different cluster centre points are achieved during each run of the algorithm. Ensemble based clustering methods resolve challenges of partition-based methods. The clustering ensembles join several partitions generated by different clustering algorithms into a single clustering solution. The proposed ensemble methodology resolves initial centroid problems and improves the efficiency of cluster results. This method finds centroid selection through overall mean distance measure. The SimRank based similarity matrix find that the bipartite graph helps to ensemble

Clustering Algorithms: An Exploratory Review

A process of similar data items into groups is called data clustering. Partitioning a Data Set into some groups based on the resemblance within a group by using various algorithms. Partition Based algorithms key idea is to split the data points into partitions and each one replicates one cluster. The performance of partition depends on certain objective functions. Evolutionary algorithms are used for the evolution of social aspects and to provide optimum solutions for huge optimization problems. In this paper, a survey of various partitioning and evolutionary algorithms can be implemented on a benchmark dataset and proposed to apply some validation criteria methods such as Root-Mean-Square Standard Deviation, R-square and SSD, etc., on some algorithms like Leader, ISODATA, SGO and PSO, and so on

A SimRank based Ensemble Method for Resolving Challenges of Partition Clustering Methods

Traditional clustering techniques alone cannot resolve all challenges of partition-based clustering methods. In the partition based clustering, particularly in variants of K-means, initial cluster centre selection is a significant and crucial point. The dependency of final cluster is totally based on initial cluster centres; hence, this process is delineated to be most significant in the entire clustering operation. The random selection of initial cluster centres is unstable, since different cluster centre points are achieved during each run of the algorithm. Ensemble based clustering methods resolve challenges of partition-based methods. The clustering ensembles join several partitions generated by different clustering algorithms into a single clustering solution. The proposed ensemble methodology resolves initial centroid problems and improves the efficiency of cluster results. This method finds centroid selection through overall mean distance measure. The SimRank based similarity matrix find that the bipartite graph helps to ensemble

Ammonia fiber explosion (AFEX) treatment of kenaf, mixed hardwood, and wheatstraw

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Unstructured Data: Qualitative Analysis

Major research is taking place in analyzing and processing of massive amounts of data produced by different organizations. The lumps of data received must be stored and different techniques are needed to visualize the numerical statistics. We have to focus on factors related to performance levels. This paper provides big data disaster management and different admission policy techniques to analyze the performance based on socio-economic factors and educational achievements along with comparison of admission policies