k-CEVCLUS: Constrained evidential clustering of large dissimilarity data
نویسندگان
چکیده
منابع مشابه
CEVCLUS: Constrained evidential clustering of proximity data
We present an improved relational clustering method integrating prior information. This new algorithm, entitled CEVCLUS, is based on two concepts: evidential clustering and constraint-based clustering. Evidential clustering uses the DempsterShafer theory to assign a mass function to each object. It provides a credal partition, which subsumes the notions of crisp, fuzzy and possibilistic partiti...
متن کاملEvidential clustering of large dissimilarity data
In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by Dempster-Shafer mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we present severa...
متن کاملCEVCLUS: evidential clustering with instance-level constraints for relational data
Recent advances in clustering consider incorporating background knowledge in the partitioning algorithm, using, e.g., pairwise constraints between objects. As a matter of fact, prior information, when available, often makes it possible to better retrieve meaningful clusters in data. Here, this approach is investigated in the framework of belief functions, which allows us to handle the imprecisi...
متن کاملk-EVCLUS: Clustering Large Dissimilarity Data in the Belief Function Framework
In evidential clustering, the membership of objects to clusters is considered to be uncertain and is represented by mass functions, forming a credal partition. The EVCLUS algorithm constructs a credal partition in such a way that larger dissimilarities between objects correspond to higher degrees of conflict between the associated mass functions. In this paper, we propose to replace the gradien...
متن کاملConstrained K-Means Clustering
We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k constraints to the underlying clusteri...
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ژورنال
عنوان ژورنال: Knowledge-Based Systems
سال: 2018
ISSN: 0950-7051
DOI: 10.1016/j.knosys.2017.11.023