Tree-based incremental association rule mining without candidate itemset generation

Pradeepini, G.; Jyothi, S.

doi:10.1109/tisc.2010.5714603

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…A considerable amount of work was dedicated to incremental derivations of FP-growth. Examples for such incremental FP-growth derivation are [33], [34], [35], [36], [37], [38], [39], [40], [41], and [42]. The main challenge these papers attempt to address is the fact that an FP-tree does not contain information about non-frequent items appearing in the originaldatabase transactions, which means that if an infrequent item becomes frequent due to the update, the original-database needs to be scanned again.…”

Section: Incremental Mining Of Frequent Itemsetsmentioning

confidence: 99%

A Guided FP-growth algorithm for multitude-targeted mining of big data

Shabtay,

Yaari,

Dattner

2018

Preprint

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In this paper we present the GFP-growth (Guided FP-growth) algorithm, a novel method for multitude-targeted mining: finding the count of a given large list of itemsets in large data. The GFP-growth algorithm is designed to focus on the specific multitude itemsets of interest and optimizes the time and memory costs. We prove that the GFPgrowth algorithm yields the exact frequency-counts for the required itemsets. We show that for a number of different problems, a solution can be devised which takes advantage of the efficient implementation of multitude-targeted mining for boosting the performance. In particular, we study in detail the problem of generating the minority-class rules from imbalanced data, a scenario that appears in many real-life domains such as medical applications, failure prediction, network and cyber security, and maintenance. We develop the Minority-Report Algorithm that uses the GFPgrowth for boosting performance. We prove some theoretical properties of the Minority-Report Algorithm and demonstrate its performance gain using simulations and real data.

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Section: Incremental Mining Of Frequent Itemsetsmentioning

confidence: 99%

A Guided FP-growth algorithm for multitude-targeted mining of big data

Shabtay,

Yaari,

Dattner

2018

Preprint

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“…Tree-based Incremental Association Rule Mining (TIARM) approach is proposed by G. Pradeepini et al [15] to reduce total database scan from 2 to 1. This approach uses INCremental (INC)-Tree data structure which is an extension of FP-Tree constructed by inserting every transaction in database one after another into it directly by sorting the transaction items according to item's appearance order in the database and together it manages the sort list.…”

Section: Association Rule Miningmentioning

confidence: 99%

Memory Cutback for FP-Tree Approach

Rana¹,

Mistry²,

Raghuwanshi³

2014

IJCA

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The pattern growth approach of association rule mining is very efficient as avoiding the candidate generation step which was utilized in Apriori algorithm. Here, revisited of the pattern growth approaches are done to improve the performance using different criteria like item search order, conditional database representation and construction approach and tree traversal ways. The header table construction is the first part in almost all the approaches having constant number of dataset items. This research is representing the reduction in overall memory requirement of pattern growth approach by reducing the search space and processor operations time at the header table generation. It is proposed to achieve the memory cutback by only considering the items that are going to be frequent and ignoring the infrequent items at early stage of scan, by considering the boundary. Experimental analysis achieves cutback in memory consumption in the proposed approach Modified FP-Growth (MFP-Growth) compare to FP-Growth and CFP-Growth.

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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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Tree-based incremental association rule mining without candidate itemset generation

Cited by 9 publications

References 4 publications

A Guided FP-growth algorithm for multitude-targeted mining of big data

A Guided FP-growth algorithm for multitude-targeted mining of big data

Memory Cutback for FP-Tree Approach

Incremental association rule mining: a survey

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