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An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhou, Hongfang Zhang, Yihui Liu, Yibin |
| Copyright Year | 2017 |
| Abstract | Categorical data clustering has attracted much attentions recently because most practical data contains categorical attributes. The k-modes algorithm, as the extension of the k-means algorithm, is one of the most widely used clustering algorithms for categorical data. In this paper, we firstly analyzed the limitations of two existing dissimilarity measures. Based on this, we proposed a novel dissimilarity measure--IID. IID considers the relationship between the object and all clusters as well as that within clusters. Finally the experiments are made on six benchmark data sets from UCI. And the corresponding results show that IID achieves better performance than two existing ones used in k-modes and KBGRD algorithms. |
| File Format | PDF HTM / HTML |
| DOI | 10.2991/iccia-17.2017.67 |
| Alternate Webpage(s) | https://download.atlantis-press.com/article/25880216.pdf |
| Alternate Webpage(s) | https://doi.org/10.2991/iccia-17.2017.67 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
