Clustering Categorical Data – Study of Mining Tools for Data Labeling

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B. V.V.S Prasad
M. Sathya Narayana,N. Siva Ram Babu, B.Suresh Kumar


Cluster analysis sampling has been recognized as a best technique to improve the efficiency of clustering. However, with sampling applied to those points which are not sampled will not have their labels after the normal process. Although there is a straightforward approach in the numerical domain, the problem of how to allocate those unlabeled data points into proper clusters remains as a challenging issue in the categorical domain. In this paper, a mechanism named Maximal Resemblance Data Labeling (abbreviated as MARDL) is proposed to allocate each unlabeled data point into the corresponding appropriate cluster based on the novel categorical clustering technique, importance of the combinations of attribute values. MARDL has two advantages: 1) MARDL exhibits high execution efficiency and 2) MARDL can achieve high intra cluster similarity and low inter cluster similarity, which are regarded as the most important properties of clusters, thus benefiting the analysis of cluster behaviors. This article analysis the implementing the proposed system using data mining tools, the algorithm which shows the effective from Rock.



Keywords: Clustering, Rock, sampling, MARDL, Data mining tools


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