Incremental maintenance of generalized association rules under taxonomy evolution

Tseng, Ming-Cheng; Lin, Wen-Yang; Jeng, Rong

doi:10.1177/0165551507082271

Cited by 10 publications

(8 citation statements)

References 21 publications

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“…Some other methods discuss interesting rules [15][16][17][18][25][26][27][28][29][30][31][32][33]. Correlation coefficient was used to mine positive and negative association rules [26,27].…”

Section: Literature Reviewmentioning

confidence: 99%

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Selecting Actionable Patterns from Positive and Negative Sequential Patterns

Liu¹,

Lv²,

Xiang³

et al. 2017

JRST

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Positive and negative sequential patterns (PNSP) play an informative role in various applications. In this paper, a new method is proposed to effectively select the actionable sequential patterns (ASP) from the PNSPs by segmenting and discriminating elements with sequence. First, it is to locally discriminate adjacent elements and incremental elements in the PNSPs. Second, globally segment and discriminate all the elements with sequences. Third, Markov process is further applied to select the ASP by measuring the interestingness of a sequence. The experimental comparisons on synthetic and real-world databases show that the proposed method is very effective to select ASPs.

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Section: Literature Reviewmentioning

confidence: 99%

“…N the literature lots of efforts have been made to discover actionable knowledge [1][2][3][4][5][6][7][8][9][10][11][12][13][14], to measure the interestingness in association rule mining [15][16][17][18] and to mine Positive Sequential Patterns (PSP) [19,20]. PSP are frequent subsequences which are uncovered from sequence database.…”

Section: Introduction Imentioning

confidence: 99%

Selecting Actionable Patterns from Positive and Negative Sequential Patterns

Liu¹,

Lv²,

Xiang³

et al. 2017

JRST

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“…If the hash table is empty, new node is created and root and tail pointing to it. Increment the counter by one and change valid bit to 1 for this item set [15].…”

Section: Hash Classmentioning

confidence: 99%

Association Rule Generation using Modified Hashing Function

Ramakrishnana¹,

Jyaraj²

2014

IJCA

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Association rule mining is one of the most interesting and challenging task in data mining process. There exists many association rule mining techniques, each having merits and demerits. The main problem that exists in many traditional association rule mining algorithms is that these algorithms need more than one database scan to generate association rules. As scanning the database is a costly operation, algorithms capable of generating association rules with only one scan is the need of the hour. In this paper, a novel algorithm for generating association rules is presented which uses hashing function. This algorithm scans the database only once by utilizing the latest version of priori algorithm, direct hashing algorithm and pruning process. The algorithm discovers set of association rules from frequent k-item sets by computing the frequency of each item set. Then pruning process is applied to minimize the number of item sets generated after scanning the size of the database. Experimental results show that our method is very effective in generating association rules without any collision, leading to very high data accuracy.

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“…However, Borders and its variants, FUP 2 and MAAP, are capable of handling the problem in this regard. Apart from the above‐mentioned algorithms, several other works can be found in the literature 38,45–58. All these algorithms exhibit their own strengths and weaknesses and are capable of handling the incremental rule mining problem.…”

Section: Incremental Association Rule Miningmentioning

confidence: 99%

Incremental association rule mining: a survey

Nath

Bhattacharyya

Ghosh

2013

WIREs Data Min & Knowl

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Association rule mining is a computationally expensive task. Despite the huge processing cost, it has gained tremendous popularity due to the usefulness of association rules. Several efficient algorithms can be found in the literature. This paper provides a comprehensive survey on the state‐of‐the‐art algorithms for association rule mining, specially when the data sets used for rule mining are not static. Addition of new data to a data set may lead to additional rules or to the modification of existing rules. Finding the association rules from the whole data set may lead to significant waste of time if the process has started from the scratch. Several algorithms have been evolved to attend this important issue of the association rule mining problem. This paper analyzes some of them to tackle the incremental association rule mining problem. © 2013 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification

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Incremental maintenance of generalized association rules under taxonomy evolution

Cited by 10 publications

References 21 publications

Selecting Actionable Patterns from Positive and Negative Sequential Patterns

Selecting Actionable Patterns from Positive and Negative Sequential Patterns

Association Rule Generation using Modified Hashing Function

Incremental association rule mining: a survey

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