ACM Computing Surveys (CSUR)
High-Order Pattern Discovery from Discrete-Valued Data
IEEE Transactions on Knowledge and Data Engineering
Biclustering of Expression Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Subspace clustering for high dimensional data: a review
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Simultaneous Feature Selection and Clustering Using Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biclustering Algorithms for Biological Data Analysis: A Survey
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
| Hi-index | 0.00 |
Statistical research in clustering has mainly focused on numerical data sets. It is difficult for existing clustering methods to apply to data sets involving nominal values. Besides, existing methods are seldom concerned with helping the users to interpret the results obtained. This paper proposes a novel clustering method that uses association patterns to obtain and characterize the clustering results. In many data mining applications such as basket analysis, association patterns have been used to capture relationship among events which can be easily understood by human. Using association patterns to describe the obtained clusters make the tasks of cluster interpretation and understanding easier. Experiments show that useful information can be readily acquired from the clustering outputs.