Quality Evaluation for Document Relation Discovery Using Citation Information

Sriphaew, Kritsada; Theeramunkong, Thanaruk

doi:10.1093/ietisy/e90-d.8.1225

Cited by 6 publications

(6 citation statements)

References 8 publications

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“…The frequent patterns describe a repeated appearance of items in a transaction. Recently, association rule mining or its derivatives have been applied in finding relations among documents [32,33]. By encoding documents as items, and terms in the documents as transactions, mined are a number of frequent patterns, each of which represents a set of documents that share common terms more than a threshold, called support.…”

Section: Related Workmentioning

confidence: 99%

“…Thereafter, as a further step, a set of frequent rules can be found based on these frequent patterns with another threshold, namely confidence. Sriphaew and Theeramunkong [32,33] proposed an approach to mine relations in scientific research publications by using association rule mining with support-confidence framework by extending a concept of traditional association rule mining to mine frequent itemsets on the database with real-valued instead of only item existences. This method could discover a set of topically similar documents as high quality relations.…”

Section: Related Workmentioning

confidence: 99%

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Region-based association measures for ranking mined news relations

Kittiphattanabawon

Theeramunkong

Nantajeewarawat

2014

IDA

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Association rule mining can be applied to discover relations among news documents. Most existing approaches may not be good enough to extract meaningful news relations due to the limitation of having only single association measure for ranking mined news relations. This paper presents a region-based method to selectively use different association measures for different ranking regions, towards improvement of the ranking mechanism for news relation discovery. In this method, first the mined relations are sorted under a preliminary criterion to form a number of regions before scoring the relations in each region with different association measures. The meaningful news relations are discovered through three levels of relation: completely related, somehow related and unrelated relations, judged by the domain expert. To evaluate the proposed regionbased ranking method, the method which has no region construction is considered to be the baseline by using a set of 1,132 news relations mined from 811 news documents. As performance criterion, a rank-order mismatch is explored to compare the qualitative results between the proposed method and the human evaluation. Compared to the baseline, the region-based method significantly improves performance by the average rank-order mismatch of 1.21%-28.32% for confidence and 4.83%-29.04% for conviction, respectively. N. Kittiphattanabawon et al. / Region-based association measures for ranking mined news relationsresulting in the complexity of O(N L ), where L is the length of the longest pattern. However, an inevitable issue of association rule mining is triggered by the large number of rules generated [5,22,23,27,31]. In the past, several works introduced various approaches to solve the problem of these tremendous association rules, which may be classified into filtering, constraint-based mining and ranking.Up to present, most previous works have applied single objective measure (such as support, confidence or lift) or single subjective measure (such as preference, personal value or cultural value) or their combinations, to filter, constrain, or rank the discovered rules. However, they still focus on a uniform function for the whole ranking interval. Intuitively, only a single objective function for the whole range of candidates may not be enough to prioritize the rules. It is worth exploring multiple objective functions for rule prioritization. Towards such issues, this paper presents a method to selectively use different objective functions (association measures) for different ranking regions to improve the ranking mechanism. So-called region-based ranking, the proposed approach, sets a specific weight for each different region (range) of the relation strength in order to maximize classification performance. This work also deals with two main issues, i.e., (1) how to determine suitable points of weight scheme change in order to form regions, and (2) which weight scheme we should assign to each region. In addition, when the domain is changed to other areas such as medical, enviro...

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Region-based association measures for ranking mined news relations

Kittiphattanabawon

Theeramunkong

Nantajeewarawat

2014

IDA

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“…Recently ARM or its derivatives have been applied in find relations among documents [15], [20]. By encoding documents as items, and terms in the documents as transactions, we mine a set of frequent patterns, each of which is in the form of a set of documents sharing common terms more than a threshold, called support.…”

Section: Association Rule Mining For Discovering News Relationsmentioning

confidence: 99%

“…Thereafter, as a further step, a set of frequent rules can be found based on these frequent patterns with another threshold, namely confidence. In this work, in order to work with non-binary data, we adopt the generalized support and generalized confidence in [15], and the generalized conviction in [20] as association measures. A formulation of the ARM task on news document relation discovery can be summarized as follows.…”

Section: Association Rule Mining For Discovering News Relationsmentioning

confidence: 99%

“…On the other hand, the task of first story detection is to identify whether a news story includes new events which are never seen. Recently, an association rule mining approach [12], [13] has been introduced for discovering document relations in scientific research publications due to its performance and scalability [14], [15]. Given N documents, cluster-based association discovery requires the calculation of all possible combination of documents, that is the complexity of O(2 N ).…”

Section: Introductionmentioning

confidence: 99%

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News Relation Discovery Based on Association Rule Mining with Combining Factors

Kittiphattanabawon

Theeramunkong

Nantajeewarawat

2011

IEICE Trans. Inf. & Syst.

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Nichnan KITTIPHATTANABAWON†a) , Student Member, Thanaruk THEERAMUNKONG †b) , and Ekawit NANTAJEEWARAWAT †c) , Members SUMMARY Recently, to track and relate news documents from several sources, association rule mining has been applied due to its performance and scalability. This paper presents an empirical investigation on how term representation basis, term weighting, and association measure affects the quality of relations discovered among news documents. Twenty four combinations initiated by two term representation bases, four term weightings, and three association measures are explored with their results compared to human judgment of three-level relations: completely related, somehow related, and unrelated relations. The performance evaluation is conducted by comparing the top-k results of each combination to those of the others using so-called rank-order mismatch (ROM). The experimental results indicate that a combination of bigram (BG), term frequency with inverse document frequency (TFIDF) and confidence (CONF), as well as a combination of BG, TFIDF and conviction (CONV), achieves the best performance to find the related documents by placing them in upper ranks with 0.41% ROM on top-50 mined relations. However, a combination of unigram (UG), TFIDF and lift (LIFT) performs the best by locating irrelevant relations in lower ranks (top-1100) with 9.63% ROM. A detailed analysis on the number of the three-level relations with regard to their rankings is also performed in order to examine the characteristic of the resultant relations. Finally, a discussion and an error analysis are given. key words: news relations, news relation discovery, association rule mining, combining factors

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Applying Latent Semantic Indexing in Frequent Itemset Mining for Document Relation Discovery

Theeramunkong

Sriphaew

Okumura

Advances in Knowledge Discovery and Data Mining

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Quality Evaluation for Document Relation Discovery Using Citation Information

Cited by 6 publications

References 8 publications

Region-based association measures for ranking mined news relations

Region-based association measures for ranking mined news relations

News Relation Discovery Based on Association Rule Mining with Combining Factors

Applying Latent Semantic Indexing in Frequent Itemset Mining for Document Relation Discovery

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