Algorithms for clustering data
Algorithms for clustering data
Understanding Quality in Conceptual Modeling
IEEE Software
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Modeling Completeness versus Consistency Tradeoffs in Information Decision Contexts
IEEE Transactions on Knowledge and Data Engineering
Exploratory Data Mining and Data Cleaning
Exploratory Data Mining and Data Cleaning
Referential integrity quality metrics
Decision Support Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
The Effects and Interactions of Data Quality and Problem Complexity on Classification
Journal of Data and Information Quality (JDIQ)
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Clustering has been a subject of wide research since it arises in many application domains. One of the clustering process issues is the evaluation of clustering results. Estimation of the obtained cluster structure quality is the main subject of cluster validity. In several years many cluster validity indexes were presented in the research community, but the general approach for clustering evaluation was not developed. In our work we are going to produce some methodology for cluster validity estimation and construct a special framework for its measure, which will combine a couple of current methods in one suitable tool. We suggest that these investigations will help a wide range of analyst in theirs work with clustering.