An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
This addresses the challenge of parameter tuning and cluster interpretability in data mining, though it appears incremental as it builds on existing multi-agent and dissimilarity-based approaches.
The paper tackles the problem of automatically discovering relevant regularities and optimal parameter configurations for clustering in datasets, resulting in an algorithm that yields consistent and interpretable clusters with comparable performance to state-of-the-art methods.
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters. Each agent operates independently by performing a Markovian random walk on a suitable weighted graph representation of the input dataset. Such a weighted graph representation is induced by the specific parameter configuration of the dissimilarity measure adopted by the agent, which searches and takes decisions autonomously for one cluster at a time. Results show that the algorithm is able to discover parameter configurations that yield a consistent and interpretable collection of clusters. Moreover, we demonstrate that our algorithm shows comparable performances with other similar state-of-the-art algorithms when facing specific clustering problems.