The difficulty of obtaining tagged corpora in order to perform Word Sense Disambiguation has led to diverse strategies. Clustering methods may be used as an initial step to discover regularities on instances, i.e. contexts of ambiguous words. In this work we evaluate a sense clustering method with a novel feature selection phase over Senseval-2 Spanish collection. The feature selection techniqu...

In recent years, combining multiple sources or views of datasets for data clustering has been a popular practice for improving clustering accuracy. As different views are different representations of the same set of instances, we can simultaneously use information from multiple views to improve the clustering results generated by the limited information from a single view. Previous studies main...

Semi-supervised clustering has recently received a lot of attention in the literature, which aims to improve the clustering performance with limited supervision. Most existing semi-supervised clustering studies assume that the data is represented in a vector space, e.g., text and relational data. When the data objects have complex structures, e.g., proteins and chemical compounds, those semi-su...

Background: In the current study, a hybrid feature selection approach involving filter and wrapper methods is applied to some bioscience databases with various records, attributes and classes; hence, this strategy enjoys the advantages of both methods such as fast execution, generality, and accuracy. The purpose is diagnosing of the disease status and estimating of the patient survival. Method...

We present an algorithm to address the problem of clustering two contextually related heterogeneous datasets that use different feature sets, but consist of non-disjoint sets of objects. The method is based on clustering the datasets individually and then combining the resulting clusters. The algorithm iteratively refines the two sets of clusters using a mutually supervised approach to maximize...

Clustering is the most common form of unsupervised learning.In clustering, it is the distribution and makeup of the data that will determine cluster membership. It needs representation of objects and similarity measure. which compares distribution of features between objects. For the high dimensionality, feature extraction and feature selection improves the performance of clustering algorithms....

This thesis is concerned with exploratory data analysis by means of Evolutionary Computation techniques. The central problem addressed is cluster analysis. The main challenges arisen from the unsupervised nature of this problem are investigated. Clustering is a problem lacking a formal general-accepted objective. This justifies the multitude of approaches proposed in literature. A review of the...

Artificial Immune Systems (AIS) can be defined as soft computing systems inspired by immune system of vertebrates. Immune system is an adaptive pattern recognition system. AIS have been used in pattern recognition, machine learning, optimization and clustering. Feature reduction refers to the problem of selecting those input features that are most predictive of a given outcome; a problem encoun...

The objective of text clustering is to divide document collections into clusters based on the similarity between documents. In this paper, an extension-based feature modeling approach towards semantically sensitive text clustering is proposed along with the corresponding feature space construction and similarity computation method. By combining the similarity in traditional feature space and th...

This paper presents a speaker clustering framework by iteratively performing two stages: a discriminative feature space is obtained given a cluster label set, and the cluster label set is updated using a clustering algorithm given the feature space. In the iterations of two stages, the cluster labels may be different from the true labels, and thus the obtained feature space based on the labels ...