A Performance Evaluation of SMCA Using Similarity Association & Proximity Coefficient Relation For Hierarchical Clustering

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)
© 2014 by IJETT Journal
Volume-15 Number-7
Year of Publication : 2014
Authors : Mr. Mayank Gupta , Mr. Ritesh Jain


Mr. Mayank Gupta , Mr. Ritesh Jain. "A Performance Evaluation of SMCA Using Similarity Association & Proximity Coefficient Relation For Hierarchical Clustering", International Journal of Engineering Trends and Technology (IJETT), V15(7),354-359 Sep 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group


Clustering techniques have a wide use and importance nowadays. This importance tends to increase as the amount of data grows and the processing power of the computers increases. Clustering applications are used extensively in various fields such as artificial intelligence, pattern recognition, economics, ecology, psychiatry and marketing.There are several algorithms and methods have been developed for clustering problem. But problem are always arises for finding a new algorithm and process for extracting knowledge for improving accuracy and efficiency. This type of dilemma motivated us to develop new algorithm and process for clustering problems. There are several another issue are also exits like cluster analysis can contribute in compression of the information included in data. In several cases, the amount of available data is very large and its processing becomes very demanding. Clustering can be used to partition data set into a number of “interesting” clusters. Then, instead of processing the data set as an entity, we adopt the representatives of the defined clusters in our process. Thus, data compression is achieved. Cluster analysis is applied to the data set and the resulting clusters are characterized by the features of the patterns that belong to these clusters. Then, unknown patterns can be classified into specified clusters based on their similarity to the clusters’ features. Useful knowledge related to our data can be extracted [1].


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Cluster, Hierarchical Clustering, Feature Matrices.