Data-clustering has been identified as a major problem in many areas. It aims to identify and extract meaningful groups from a very large set of data. It is a combinatorial problem, because the number of partitions that can be obtained grows exponentially with the volume of data to be classified and the number of clusters. In this paper, we deal with the problem from the perspective of distributed optimization and we present a new approach for dataclustering based on artificial ant colonies with control of emergence. The features of the proposed approach consist essentially in the definition of a new dynamics of ants, governed by new probabilistic rules to pick up and drop objects. A mechanism for controlling the emergence was implemented by using anti-clustering agents, and guided by the number of detected clusters. A multi-agent platform was used to implement the proposed approach. The obtained results in terms of internal and external performance measures on a set of real and synthetic benchmarks show the competitiveness of the proposed approach compared to other approaches in the literature, as well as the made modifications (contribution).
The clustering process is used to identify cancer subtypes based on gene expression and DNA methylation datasets, since cancer subtype information is critically important for understanding tumor heterogeneity, detecting previously unknown clusters of biological samples, which are usually associated with unknown types of cancer will, in turn, gives way to prescribe more effective treatments for patients. This is because cancer has varying subtypes which often respond disparately to the same treatment. While the DNA methylation database is extremely large-scale datasets, running time still remains a major challenge. Actually, traditional clustering algorithms are too slow to handle biological high-dimensional datasets, they usually require large amounts of computational time. The proposed clustering algorithm extraordinarily overcomes all others in terms of running time, it is able to rapidly identify a set of biologically relevant clusters in large-scale DNA methylation datasets, its superiority over the others has been demonstrated regarding its relative speed.
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