Mining frequent correlated graphs with a new measure

Samiullah, M.; Ahmed, Chowdhury Farhan; Fariha, Anna; Islam, Md. Rafiqul; Lachiche, Nicolas

doi:10.1016/j.eswa.2013.08.082

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…As one of the fundamental graph mining algorithms, Gaston [6] is known as the most efficient method in terms of runtime speed. The algorithm, which is an effective integration of multiple enumeration methods for path, free-tree, and cyclic graph forms, improves its mining performance by utilizing its own special techniques and list-based data structure.…”

Section: Related Workmentioning

confidence: 99%

“…Since the concept of frequent graph pattern mining was proposed to overcome the limitations of traditional frequent pattern mining approaches that perform mining operations with respect to transaction databases composed of simple items only, a variety of methods have been devised [1,3,5,6] by suggesting novel techniques for performance improvement or effectively integrating graph mining with other mining fields. However, previous graph mining methods have faced problems that cannot consider the following issues in the real world: 1) the rare item problem [4] that not only items or patterns with high supports but also ones with relatively low supports may have valuable information, and 2) the different importance problem [7,8] that elements obtained from the real world have their own importance or weights different from one another according to their characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Mining Frequent Graph Patterns Considering Both Different Importance and Rarity of Graph Elements

Lee

Yun

2015

Computer Science and Its Applications

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Abstract. Since frequent graph pattern mining was proposed, various approaches have been suggested by devising efficient techniques or integrating graph mining with other mining areas. However, previous methods have limitations that cannot reflect the following important characteristics in the real world to their mining processes. First, elements in the real world have their own importance as well as frequency, but traditional graph mining methods do not consider such features. Second, various elements composing graph databases may need thresholds different from one another according to their characteristics. However, since traditional approaches mine graph patterns on the basis of only a single threshold, losses of important pattern information can be caused. Motivated by these problems, we propose a new graph mining algorithm that can consider both different importance and multiple thresholds for each element of graphs. We also demonstrate outstanding performance of the proposed algorithm by comparing ours with previous state-of-the-art approaches.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Frequent Graph Patterns Considering Both Different Importance and Rarity of Graph Elements

Lee

Yun

2015

Computer Science and Its Applications

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“…Data mining techniques discover potentially interesting and useful knowledge from databases which cannot be extracted trivially (Ahmed, Tanbeer, Jeong, & Choi, 2012;Samiullah, Ahmed, Fariha, Islam, & Lachiche, 2014;Zaki & Meira, 2014). Relational data mining (Džeroski & Lavrač, 2001;Kavurucu, Senkul, & Toroslu, 2009;Maervoet, Vens, Berghe, Blockeel, & Causmaecker, 2012) is an important field of data mining which discovers knowledge from relational databases.…”

Section: Introductionmentioning

confidence: 98%

Flexible propositionalization of continuous attributes in relational data mining

Ahmed

Lachiche

Charnay

et al. 2015

Expert Systems with Applications

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“…Therefore, support values for edges are not counted. Vertices that occur multiple times in a graph transaction are counted once [34,35,44]. After the database scanning work is finished, the proposed algorithm scans information of length-decreasing support constraints corresponding to the given graph database and multiple minimum support constraints for the elements composing the database.…”

Section: Overall Architecture Of the Proposed Methodsmentioning

confidence: 99%

The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs

2016

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Frequent graph mining has been proposed to find interesting patterns (i.e., frequent sub-graphs) from databases composed of graph transaction data, which can effectively express complex and large data in the real world. In addition, various applications for graph mining have been suggested. Traditional graph pattern mining methods use a single minimum support threshold factor in order to check whether or not mined patterns are interesting. However, it is not a sufficient factor that can consider valuable characteristics of graphs such as graph sizes and features of graph elements. That is, previous methods cannot consider such important characteristics in their mining operations since they only use a fixed minimum support threshold in the mining process. For this reason, in this paper, we propose a novel graph mining algorithm that can consider various multiple, minimum support constraints according to the types of graph elements and changeable minimum support conditions, depending on lengths of graph patterns. In addition, the proposed algorithm performs in mining operations more efficiently because it can minimize duplicated operations and computational overheads by considering symmetry features of graphs. Experimental results provided in this paper demonstrate that the proposed algorithm outperforms previous mining approaches in terms of pattern generation, runtime and memory usage.

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Mining frequent correlated graphs with a new measure

Cited by 8 publications

References 27 publications

Mining Frequent Graph Patterns Considering Both Different Importance and Rarity of Graph Elements

Mining Frequent Graph Patterns Considering Both Different Importance and Rarity of Graph Elements

Flexible propositionalization of continuous attributes in relational data mining

The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs

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