An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

Zhang, Jin; Wei, Qiang

doi:10.1016/j.knosys.2011.08.013

Cited by 6 publications

(4 citation statements)

References 53 publications

(57 reference statements)

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“…Similar to the works in [37], this study generalizes the projection and natural join operations in traditional database to the fuzzy databases, as below. Here, given a relation , Θ denotes a set of attributes in (i.e., Θ ⊂ ), and [Θ] denotes the composite of values in tuple over attribute Θ.…”

Section: Approximate Lossless Join Decompositionmentioning

confidence: 97%

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Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

Liu

2014

Journal of Applied Mathematics

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Functional dependency is the basis of database normalization. Various types of fuzzy functional dependencies have been proposed for fuzzy relational database and applied to the process of database normalization. However, the problem of achieving lossless join decomposition occurs when employing the fuzzy functional dependencies to database normalization in an extended possibilitybased fuzzy data models. To resolve the problem, this study defined fuzzy functional dependency based on a notion of approximate equality for extended possibility-based fuzzy relational databases. Examples show that the notion is more applicable than other similarity concept to the research related to the extended possibility-based data model. We provide a decomposition method of using the proposed fuzzy functional dependency for database normalization and prove the lossless join property of the decomposition method.

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Section: Approximate Lossless Join Decompositionmentioning

confidence: 97%

“…The transitivity of similarity measure is important to any operation involving redundancy removal or tuple merging. Besides, the measure of transitivity can be applied to clustering methods or data groupings, such as the ones in [36,37]. Proposition 6.…”

Section: Proposition 5 the Approximate Equality Can Be Used To Classmentioning

confidence: 99%

Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

Liu

2014

Journal of Applied Mathematics

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“…Totad et al [38] used a batch incremental processing algorithm to obtain a FP-tree, which took less time for constructing FP-tree. Zhang et al [43] introduced an incremental method to decision support systems (DSS). The incremental method grouped the obtained results into equivalence classes so that the time and space complexity could be improved.…”

Section: Related Workmentioning

confidence: 99%

Clustering-Based Topical Web Crawling for Topic-Specific Information Retrieval Guided by Incremental Classifier

Peng

Liu

2015

Int. J. Soft. Eng. Knowl. Eng.

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Today more and more information on the Web makes it di±cult to get domain-speci¯c information due to the huge amount of data sources and the keywords that have few features. Anchor texts, which contain a few features of a speci¯c topic, play an important role in domain-speci¯c information retrieval, especially in Web page classi¯cation. However, the features contained in anchor texts are not informative enough. This paper presents a novel incremental method for Web page classi¯cation enhanced by link-contexts and clustering. Directly applying the vector of anchor text to a classi¯er might not get a good result because of the limited amount of features. Link-context is used¯rst to obtain the contextual information of the anchor text. Then, a hierarchical clustering method is introduced to cluster feature vectors and content unit, which increases the length of a feature vector belonging to one speci¯c class. Finally, incremental SVM is proposed to get the¯nal classi¯er and increase the accuracy and e±ciency of a classi¯er. Experimental results show that the performance of our proposed method outperforms the conventional topical Web crawler in Harvest rate and Target recall.

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“…Clustering is an important method in identifying the natural structures of datasets [1]. As a fundamental technique in data mining [2,3], clustering analysis aims at dividing data objects into several groups such that data objects in each group are similar to one another and dissimilar to data objects in different groups [4,5]. Over the years, clustering algorithms are widely used in data analysis in different domains, such as text data [6,7], customer data [8,9], image data [10,11] and medical data [12,13].…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithm Based on Sparse Feature Vector without Specifying Parameter

2020

Teh. vjesn.

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An efficient incremental method for generating equivalence groups of search results in information retrieval and queries

Cited by 6 publications

References 53 publications

Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

Lossless Join Decomposition for Extended Possibility-Based Fuzzy Relational Databases

Clustering-Based Topical Web Crawling for Topic-Specific Information Retrieval Guided by Incremental Classifier

Clustering Algorithm Based on Sparse Feature Vector without Specifying Parameter

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