Noise-enhanced clustering and competitive learning algorithms

Corrigendum to "Noise enhanced clustering and competitive learning algorithms" [Neural Networks 37 (2013) 132-140]

A Multi-Objective Approach to Fuzzy Clustering using ITLBO Algorithm

Data clustering is one of the most important areas of research in data mining and knowledge discovery. Recent research in this area has shown that the best clustering results can be achieved using multi-objective methods. In other words, assuming more than one criterion as objective functions for clustering data can measurably increase the quality of clustering. In this study, a model with two ...

A Reinforcement Learning Approach to Online Clustering

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) ...

An Effective Approach for Robust Metric Learning in the Presence of Label Noise

Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...

Assessment of the Performance of Clustering Algorithms in the Extraction of Similar Trajectories

In recent years, the tremendous and increasing growth of spatial trajectory data and the necessity of processing and extraction of useful information and meaningful patterns have led to the fact that many researchers have been attracted to the field of spatio-temporal trajectory clustering. The process and analysis of these trajectories have resulted in the extraction of useful information whic...