This paper introduces a new description-centric algorithm for web document clustering based on the hybridization of the Global-Best Harmony Search with the K-means algorithm, Frequent Term Sets and Bayesian Informatio...
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ISBN:
(纸本)9781424481262
This paper introduces a new description-centric algorithm for web document clustering based on the hybridization of the Global-Best Harmony Search with the K-means algorithm, Frequent Term Sets and Bayesian Information Criterion. The new algorithm defines the number of clusters automatically. The Global-Best Harmony Search provides a global strategy for a search in the solution space, based on the Harmony Search and the concept of swarm intelligence. The K-means algorithm is used to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called IGBHSK, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then evaluated by a group of users.
This paper introduces a new description-centric algorithm for web document clustering based on Memetic algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines th...
详细信息
ISBN:
(纸本)9781424481262
This paper introduces a new description-centric algorithm for web document clustering based on Memetic algorithms with Niching Methods, Term-Document Matrix and Bayesian Information Criterion. The algorithm defines the number of clusters automatically. The Memetic algorithm provides a combined global and local strategy for a search in the solution space and the Niching methods to promote diversity in the population and prevent the population from converging too quickly (based on restricted competition replacement and restrictive mating). The Memetic algorithm uses the K-means algorithm to find the optimum value in a local search space. Bayesian Information Criterion is used as a fitness function, while FP-Growth is used to reduce the high dimensionality in the vocabulary. This resulting algorithm, called WDC-NMA, was tested with data sets based on Reuters-21578 and DMOZ, obtaining promising results (better precision results than a Singular Value Decomposition algorithm). Also, it was also then initially evaluated by a group of users.
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