Split-Merge Evolutionary Clustering for Multi-View Streaming Data

Multi-objective evolutionary algorithms for data clustering

In this work we investigate the use of Multi-Objective metaheuristics for the data mining task of clustering. We first investigate methods of evaluating the quality of clustering solutions, we then propose a new Multi-Objective clustering algorithm driven by multiple measures of cluster quality and then perform investigations into the performance of different Multi-Objective clustering algorith...

Multi-Task Multi-View Clustering for Non-Negative Data

Multi-task clustering and multi-view clustering have severally found wide applications and received much attention in recent years. Nevertheless, there are many clustering problems that involve both multi-task clustering and multi-view clustering, i.e., the tasks are closely related and each task can be analyzed from multiple views. In this paper, for non-negative data (e.g., documents), we int...

Probabilistic Multi-State Split-Merge Algorithm for Coupling Parameter Estimates

A new approach to finding good local maxima of the likelihood function based on synthesizing information from two local maxima is presented. We investigate the coupled EM algorithm (CoEM) for coupling local maxima solutions from two separate EM runs for the multinomial mixture model. The CoEM algorithm probabilistically splits and merges multiple latent states based on conditional independence ...

Split-and-merge Tweak in Cross Entropy Clustering

Unsupervised Semantic Role Induction via Split-Merge Clustering

In this paper we describe an unsupervised method for semantic role induction which holds promise for relieving the data acquisition bottleneck associated with supervised role labelers. We present an algorithm that iteratively splits and merges clusters representing semantic roles, thereby leading from an initial clustering to a final clustering of better quality. The method is simple, surprisin...