Abstract
Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in the data sets. In this paper, we propose to provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering, convex or non-convex. The method is based on layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge. Encourage result show the effectiveness of the proposed technique.
Highlights
Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data and is broadly recognized as a useful tool in many applications
In this work a new clustering methodology has been introduced based on the layered structure representation that be obtained from measurement distance and angle of numerical data to the centroid data and based on the iterative clustering construction utilizing a nearest neighbor distance between clusters to merge
It is found that the proposed provide a consistent partitioning of a dataset which allows identifying any shape of cluster patterns in case of numerical clustering as well as condensed clustering
Summary
Clustering is one of the most useful tasks in data mining process for discovering groups and identifying interesting distributions and patterns in the underlying data and is broadly recognized as a useful tool in many applications. It has been subject of wide research since it arises in many application domains in engineering, business and social sciences. Partitional clustering attempts to directly decompose the data set into a set of disjoint clusters By iterative such partitioning, K-means minimizes the sum of distance from each data to its clusters. K-means minimizes the sum of distance from each data to its clusters This method ability to cluster huge data, and outliers, quickly and efficiently[4]. The proposed method, in particular, for a shape independent clustering that can be applied in the case of condensed clustering
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