DCC: a framework for dynamic granular clustering

Abstract

Clustering is one of the most relevant data mining tasks. Its goal is to group similar objects in one cluster while dissimilar objects should belong to different clusters. Many extensions have been developed based on traditional cluster algorithms. Recently, approaches for dynamic as well as for granular clustering have been of particular interest. This paper provides a framework, DCCDynamic Clustering Cube, to categorize existing dynamic granular clustering algorithms. Furthermore, the DCCFramework can be used as a research map and starting point for new developments in this area.

Fuzzy information granulation towards interpretable sentiment analysis

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DGCC: data-driven granular cognitive computing

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Exploratory multivariate analysis for empirical information affected by uncertainty and modeled in a fuzzy manner: a review

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