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Divide and ConquerMethod for Clustering Mixed Numerical and Categorical Data
| Content Provider | Semantic Scholar |
|---|---|
| Author | Murala, Dileep Kumar |
| Copyright Year | 2013 |
| Abstract | Clustering is a challenging task in data mining technique. The aim of clustering is to group the similar data into number of clusters. Various clustering algorithms have been developed to group data into clusters. The main aim of cluster analysis is to assign objects into groups (clusters) in such a way that two objects from the same cluster are more similar than two objects from different clusters. Various clustering algorithms have been developed to group data into clusters in diverse domains. However, these clustering algorithms work effectively either on pure numeric data or on pure categorical data, most of them perform poorly on mixed categorical and numeric data types. In this paper we cluster the mixed numeric and categorical data set in efficient manner. In this paper, we propose a divide-and-conquer technique to solve this problem. First, the original mixed dataset is divided into two sub-datasets: the pure categorical dataset and the pure numeric dataset. Next, existing well established clustering algorithms designed for different types of datasets are employed to produce corresponding clusters. Last, the clustering results on the categorical and numeric dataset are combined as a categorical dataset, on which the categorical data clustering algorithm is used to get the final clusters. Keywords--clustering, novel divide-and-conquer, mixed dataset, Numerical data, and categorical data. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.ijcsit.com/docs/Volume%204/Vol4Issue1/ijcsit2013040124.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
