A new algorithm for clustering search results

Mecca, Giansalvatore; Raunich, Salvatore; Pappalardo, Alessandro

doi:10.1016/j.datak.2006.10.006

Cited by 91 publications

(68 citation statements)

References 24 publications

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“…A new algorithm for clustering search results for search engines is proposed in [24]. Many other clustering systems analyze snippets, a short document abstract returned by search engines, post-processing step.…”

Section: Review Of Semantic Driven Document Clustering Methodsmentioning

confidence: 99%

Semantic based Document Clustering: A Detailed Review

Shah¹,

Mahajan²

2012

IJCA

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Section: Review Of Semantic Driven Document Clustering Methodsmentioning

confidence: 99%

Semantic based Document Clustering: A Detailed Review

Shah¹,

Mahajan²

2012

IJCA

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“…This process can be carried out using a variety of existing algorithms, among them Lingo [11], STC [8], SHOC [12], Dynamic SVD [14]. But, because it should be improve the usefulness of the groupings and clarity of the labeling-as it was mentioned above-, a new algorithm called Agents-WDC was developed.…”

Section: Cluster Construction and Labelingmentioning

confidence: 99%

“…This algorithm transforms the document clustering problem from an un-supervised problem to a semi-supervised problem using must-link and cannot-link relations between documents. In 2007, the Dynamic SVD clustering (DSC) [14] [15]. These algorithms use combinations of frequent words (association rules approach) shared in the documents to measure their proximity in the text clustering process.…”

Section: Related Workmentioning

confidence: 99%

“…An example of this type of algorithm is Suffix Tree Clustering (STC) [8], which incrementally creates labels easily understood by users, based on common phrases that appear in the documents. Description-centric algorithms [1,7,[11][12][13][14][15] are designed specifically for web document clustering, seeking a balance between the quality of clusters and the description (labeling) of clusters. An example of such algorithms is Lingo [11] (implemented by www.carrot2.org), which makes use of Singular Value Decomposition (SVD) to find the best relationships between terms, but groups the documents based on the most frequent phrases in the document collection.…”

Section: Introductionmentioning

confidence: 99%

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TopicSearch - Personalized Web Clustering Engine Using Semantic Query Expansion, Memetic Algorithms and Intelligent Agents

et al. 2013

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Abstract-As resources become more and more available on the Web, so the difficulties associated with finding the desired information increase. Intelligent agents can assist users in this task since they can search, filter and organize information on behalf of their users. Web document clustering techniques can also help users to find pages that meet their information requirements. This paper presents a personalized web document clustering called TopicSearch. TopicSearch introduces a novel inverse document frequency function to improve the query expansion process, a new memetic algorithm for web document clustering, and frequent phrases approach for defining cluster labels. Each user query is handled by an agent who coordinates several tasks including query expansion, search results acquisition, preprocessing of search results, cluster construction and labeling, and visualization. These tasks are performed by specialized agents whose execution can be parallelized in certain instances. The model was successfully tested on fifty DMOZ datasets. The results demonstrated improved precision and recall over traditional algorithms (k-means, Bisecting k-means, STC y Lingo). In addition, the presented model was evaluated by a group of twenty users with 90% being in favor of the model. Index Terms-Web document clustering, intelligent agents, query expansion, WordNet, memetic algorithms, user profile.

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“…Cluster analysis has been a hot topic of research due to its applicability in many disciplines including market segmentation [1], image processing [2], web mining [3], and bio-informatics [4], [5] to name just a few. The Clustering Problemcan be defined as a tuple(X, A, R, Φ) where:…”

Section: Introductionmentioning

confidence: 99%

Enhanced Genetic Algorithm with K-Means for the Clustering Problem

Bouhmala¹,

Viken²,

Lonnum³

2015

IJMO

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Abstract-In this paper, an algorithm for the clustering problem using a combination of the genetic algorithm with the popular K-Means greedy algorithm is proposed. The main idea of this algorithm is to use the genetic search approach to generate new clusters using the famous two-point crossover and then apply the K-Means technique to further improve the quality of the formed clusters in order to speed up the search process.Experimental results demonstrate that the proposed genetic algorithm combined with K-Means converges faster while producing the same quality of the clustering compared to the standard genetic algorithm.

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A new algorithm for clustering search results

Cited by 91 publications

References 24 publications

Semantic based Document Clustering: A Detailed Review

Semantic based Document Clustering: A Detailed Review

TopicSearch - Personalized Web Clustering Engine Using Semantic Query Expansion, Memetic Algorithms and Intelligent Agents

Enhanced Genetic Algorithm with K-Means for the Clustering Problem

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