A Comparative Study on Hierarchical Clustering in Data Mining
Data mining is largely concerned with building models. Model is simply an algorithm or set of rules that connects a collection of data (input) to a particular target or outcome. Data mining involves the tasks are classification, estimation, prediction, clustering, affinity grouping, description & profiling. The first 3 are all the examples of directed data mining, where the goal is to find the value of a particular target variable. Affinity grouping and clustering are undirected tasks where the goal is to uncover structure in data without respect to particular target variable.
Profiling in a descriptive task that may be either directed or undirected. In this paper we will review the main methods and approaches of clustering. Clustering is the task of segmenting a heterogeneous population into a number of more homogeneous sub groups or clusters. This survey concentrated on data mining, data mining issues, clusters, clustering, clustering analysis, clustering algorithms, clustering issues, comparison of clustering algorithm, and Requirements of clustering in data mining.
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