Semi-supervised Clustering Ensemble Evolved by Genetic Algorithm for Web Video Categorization

Genetic Algorithms GAs have been widely used in optimization problems for their high ability in seeking better and acceptable solutions within limited time. Clustering ensemble has emerged as another flavor of optimal solutions for generating more stable and robust partition from existing clusters. GAs have proved a major contribution to find consensus cluster partitions during clustering ensemble. Currently, web video categorization has been an ever challenging research area with the popularity of the social web. In this paper, we propose a framework for web video categorization using their textual features, video relations and web support. There are three contributions in this research work. First, we expand the traditional Vector Space Model VSM in a more generic manner as Semantic VSM S-VSM by including the semantic similarity between the feature terms. This new model has improved the clustering quality in terms of compactness high intra-cluster similarity and clearness low inter-cluster similarity. Second, we optimize the clustering ensemble process with the help of GA using a novel approach of the fitness function. We define a new measure, Pre-Paired Percentage PPP, to be used as the fitness function during the genetic cycle for optimization of clustering ensemble process. Third, the most important and crucial step of the GA is to define the genetic operators, crossover and mutation. We express these operators by an intelligent mechanism of clustering ensemble. This approach has produced more logical offspring solutions. Above stated all three contributions have shown remarkable results in their corresponding areas. Experiments on real world social-web data have been performed to validate our new incremental novelties.