A New Algorithm for Selection of Better K Value Using Modified Hill Climbing in K-Means Algorithm

Provided by: Journal of Theoretical and Applied Information Technology
Topic: Data Management
Format: PDF
Clustering is an important tool for a variety of applications in data mining, data compression, statistical data analysis and vector quantization which is a process of grouping objects with similar properties. Any cluster should exhibit two main properties; low inter-class similarity and high intra-class similarity. Clustering algorithms are mainly divided into two categories: hierarchical algorithm and partition algorithm. A hierarchical clustering algorithm divides the given data set into smaller subsets in hierarchical fashion. A partition clustering algorithm partitions the data set into desired number of sets in a single step. Clustering has a long and rich history in a variety of scientific fields and many methods have been proposed to solve clustering problem.

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