Abstract
Consensus clustering algorithm, which integrates several clustering results obtained by common algorithms, can find a better result that is independent on parameter settings. However, this kind of algorithm is often designed based on simple, such as $K$ -means, algorithms, which is limited by the time complexity. In this work, a P system, a novel branch of bio-inspired computing with inherent parallel and distributed computation, is combined with the consensus clustering algorithm. As a result, an improved consensus clustering algorithm is constructed using the hierarchical membrane structure and parallel evolution mechanism in a cell-like P system with multi-catalysts, where the catalysts are utilized to regulate the parallelism of objects evolution. The integration strategy of the algorithm is based on a revised PAM where only the $q$ -nearest neighbors of the original medoids are considered as candidates for the new medoids. The experimental results indicate that the clustering quality of the proposed algorithm is more robust than well-known consensus clustering algorithms on data sets with noises and outliers. This work gives evidence that the effectiveness and efficiency of consensus clustering algorithms can be improved using P systems.
Highlights
Information plays an important role in each field in modern society
1) A revised PAM is proposed in which only the q-nearest neighbors of the original medoids are considered as candidates of a possible replacement for the original medoids, which improves the computational efficiency. This medoid replacement approach is used as a basic partitionings (BPs) integration strategy of the consensus clustering algorithm which is robust to noises and outliers
While the time complexity of K means-based consensus clustering algorithm (KCC) is O(InrK ), where I is the number of iterations of the K -means clustering approach, n is the size of the dataset, r is the number of BPs and K is the true number of clusters
Summary
Information plays an important role in each field in modern society. Analyzing data and extracting useful information from huge amounts of data are always important topics in both research and practice. How to improve algorithm quality within an acceptable time complexity is an important research topic in consensus clustering This work addresses this problem using a bio-inspired computing method, i.e., a P system. This work follows the direct membrane algorithm approach and chooses the cell-like P system with multicatalysts (MCC-P), a specific cell-like P system, to develop and to improve a consensus clustering algorithm based on PAM (Prediction Around Medoids) [45] with the q-nearest neighbors. 1) A revised PAM is proposed in which only the q-nearest neighbors of the original medoids are considered as candidates of a possible replacement for the original medoids, which improves the computational efficiency This medoid replacement approach is used as a BP integration strategy of the consensus clustering algorithm which is robust to noises and outliers.
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