Determinantal consensus clustering
Random restart of a given algorithm produces many partitions to yield a consensus clustering. Ensemble methods such as consensus clustering have been recognized as more robust approaches for data clustering than single clustering algorithms. We propose the use of determinantal point processes or DPP for the random restart of clustering algorithms based on initial sets of center points, such as k-medoids or k-means. The relation between DPP and kernel-based methods makes DPPs suitable to describe and quantify similarity between objects. DPPs favor diversity of the center points within subsets. So, subsets with more similar points have less chances of being generated than subsets with very distinct points. The current and most popular sampling technique is sampling center points uniformly at random. We show through extensive simulations that, contrary to DPP, this technique fails both to ensure diversity, and to obtain a good coverage of all data facets. These two properties of DPP are key to make DPPs achieve good performance with small ensembles. Simulations with artificial datasets and applications to real datasets show that determinantal consensus clustering outperform classical algorithms such as k-medoids and k-means consensus clusterings which are based on uniform random sampling of center points.
Code (0)
등록된 구현이 없습니다.
Tasks
ClusteringClustering EnsembleDiversityPoint ProcessesSimilar Papers 제목 키워드 기반
Notes on using Determinantal Point Processes for Clustering with Applications to Text Clustering
In this paper, we compare three initialization schemes for the KMEANS clustering algorithm: 1) random initialization (KMEANSRAND), 2) KMEANS++, and 3) KMEANSD++. Both KMEANSRAND and KMEANS++ have a major that the value o…
ClusteringPoint ProcessesText ClusteringDeep Clustering With Consensus Representations
The field of deep clustering combines deep learning and clustering to learn representations that improve both the learned representation and the performance of the considered clustering method. Most existing deep cluster…
ClusteringClustering EnsembleDeep ClusteringFast Determinantal Point Process Sampling with Application to Clustering
Determinantal Point Process (DPP) has gained much popularity for modeling sets of diverse items. The gist of DPP is that the probability of choosing a particular set of items is proportional to the determinant of a posit…
ClusteringConsensus Clustering: An Embedding Perspective, Extension and Beyond
Consensus clustering fuses diverse basic partitions (i.e., clustering results obtained from conventional clustering methods) into an integrated one, which has attracted increasing attention in both academic and industria…
ClusteringConstrained ClusteringDomain Adaptationfeature selection+3Robust Consensus Clustering and its Applications for Advertising Forecasting
Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions. In practice, there exist noise and outliers in clustering task, which, however…
Clustering