Finding Clusters and Outliers for Data Sets with Constraints
In this paper, we present our research on data mining approaches with the existence of obstacles. Although there are a lot of algorithms designed to detect clusters with obstacles, few algorithms can detect clusters and outliers simultaneously and interactively. We here extend our original research...
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| Format: | Article |
| Language: | English |
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De Gruyter
2011-04-01
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| Series: | Journal of Intelligent Systems |
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| Online Access: | https://doi.org/10.1515/jisys.2011.001 |
| Summary: | In this paper, we present our research on data mining approaches with the existence of obstacles. Although there are a lot of algorithms designed to detect clusters with obstacles, few algorithms can detect clusters and outliers simultaneously and interactively. We here extend our original research [Shi, Zhang, Towards Exploring Interactive Relationship between Clusters and Outliers in Multi-Dimensional Data Analysis, 518–519: IEEE Computer Society, 2005] on iterative cluster and outlier detection to study the problem of detecting cluster and outliers iteratively with the presence of obstacles. Clusters and outliers are concepts of the same importance, so it is necessary to treat clusters and outliers in the same way in data analysis. In this algorithm, clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. The adjustment and modification of the clusters and outliers are performed iteratively until a certain termination condition is reached. This data processing algorithm can be applied in many fields such as pattern recognition, data clustering and signal processing. |
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| ISSN: | 0334-1860 2191-026X |