A New Approach to Search Result Clustering and Labeling

Turel, Anil; Can, Fazlı

doi:10.1007/978-3-642-25631-8_26

Cited by 7 publications

(6 citation statements)

References 14 publications

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“…We provide experimental results by systematically evaluating the performance of our method in the AMBIENT [7] and ODP-239 [8] test collections. We show that our method can successfully achieve both clustering and labeling tasks [19].…”

Section: Introductionmentioning

confidence: 87%

“…Before passing to the clustering phase, we index each document using its terms that appear in the term list. The term weights are computed by using the log entropy formula [10] [19]. Entropy based term weighting considers the distribution of term over documents.…”

Section: Preprocessingmentioning

confidence: 99%

“…Finally, we reduce the weights of singleword terms by multiplying them with a constant value, in our experiments 0.3, and to increase the importance of phrases, they are multiplied with 0.7. Then, we normalize the term weights of documents and the collection becomes ready for clustering [19].…”

Section: Preprocessingmentioning

confidence: 99%

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Information Retrieval Technology

Salem¹,

Shaalan²,

Oroumchian³

et al. 2011

Lecture Notes in Computer Science

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Search engines present query results as a long ordered list of web snippets divided into several pages. Post-processing of retrieval results for easier access of desired information is an important research problem. In this paper, we present a novel search result clustering approach to split the long list of documents returned by search engines into meaningfully grouped and labeled clusters. Our method emphasizes clustering quality by using cover coefficient-based and sequential k-means clustering algorithms. A cluster labeling method based on term weighting is also introduced for reflecting cluster contents. In addition, we present a new metric that employs precision and recall to assess the success of cluster labeling. We adopt a comparative strategy to derive the relative performance of the proposed method with respect to two prominent search result clustering methods: Suffix Tree Clustering and Lingo. Experimental results in the publicly available AMBIENT and ODP-239 datasets show that our method can successfully achieve both clustering and labeling tasks.

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Section: Introductionmentioning

confidence: 87%

Section: Preprocessingmentioning

confidence: 99%

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Information Retrieval Technology

Salem¹,

Shaalan²,

Oroumchian³

et al. 2011

Lecture Notes in Computer Science

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“…Authors propose a method where search result clustering is performed using cover coefficient and sequential k-means method 13 . The clusters are then labeled using term weighting.…”

Section: Related Workmentioning

confidence: 99%

Labeling of Web Search Result Clusters Using Heuristic Search and Frequent Itemset

Alam

Sadaf

2015

Procedia Computer Science

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Clustering of search result is undoubtedly a tool that can provide the summarization of the millions of documents in a way where a user can easily locate his/her information. To guide user to the right cluster of documents, cluster labels should be meaningful and correctly representing the clusters. However significant a cluster is, if the label is not proper, user will never select it. In this paper, we present a method to label clusters based on their linking information. Our cluster labeling method is independent of any clustering method but restricted to only search result documents. We use heuristic search method to find all the linked documents of a cluster. If all or some documents of a cluster share hyperlinks, then we deduce label from these linked documents' titles using famous Apriori algorithm for frequent itemset mining. This removes the requirement of reviewing other members of a cluster in labeling process.

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“…The empirical result on various datasets used in their work like sunflower, wine, iris, ring, etc., shows the efficiency of their approach. Turel and Can (2011) introduced the cluster labelling method using cover coefficient-based and sequential k-means algorithm. Cluster labelling is done based on term weighting.…”

Section: Literature Surveymentioning

confidence: 99%

Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach

Roul

Sahay

2017

IJISDC

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Cluster labelling is a technique which provides useful information about the cluster to the end users. In this paper, we propose a novel approach which is the follow-up of our previous work. Our earlier approach generates clusters of web documents by using a modified apriori approach which is more efficient and faster than the traditional apriori approach. To label the clusters, the propose approach used an effective feature selection technique which selects the top features of a cluster. Rather than labelling the cluster with 'bag of words', a concept driven mechanism has been developed which uses the Wikipedia that takes the top features of a cluster as input to generate the possible candidate labels. Mutual information (MI) score technique has been used for ranking the candidate labels and then the topmost candidates are considered as potential labels of a cluster. Experimental results on two benchmark datasets demonstrate the efficiency of our approach.Keywords: candidate label; chi-square; keyword ranking; mutual information; Wikipedia.Reference to this paper should be made as follows: Roul, R.K. and Sahay, S.K. (2017) 'Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach', Int.

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A New Approach to Search Result Clustering and Labeling

Cited by 7 publications

References 14 publications

Information Retrieval Technology

Information Retrieval Technology

Labeling of Web Search Result Clusters Using Heuristic Search and Frequent Itemset

Cluster labelling using chi-square-based keyword ranking and mutual information score: a hybrid approach

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