Incremental association rule mining: a survey

Nath, Bhabesh; Bhattacharyya, Dhruba K.; Ghosh, Ashish

doi:10.1002/widm.1086

Cited by 45 publications

(18 citation statements)

References 53 publications

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“…It is calculated as follows 3Where Precision is the count of the positive results that are correct divided with that of the reults that are positive and results that are negative. (4) and Recall is the count of the results that are positive which are divided by the count of all appropriate samples and are denoted below as (5) In terms of Time complexity, the performance metrics considered the average case time which is calculated as follows Let be the execution time for all possible inputs of the size ' n'and be the probabilities of these inputs , then the average-case time complexity is measured as (6) IV DATA AND EXPERIMENTAL SETUP The information were taken from the UCI store [13] . These information are gradual in nature and are Car eval.…”

Section: Logarithmic Lossmentioning

confidence: 99%

“…In most certifiable applications like securities exchange trade, online exchange, retail promoting, and banking, information for the most part are refreshed regularly, just as, new information are produced and old information might be out of date with the advancement of time. Thus, productive gradual refreshing calculations are required for support of the found affiliation principles to abstain from re-trying mining all in all refreshed database and in this manner dealing with the steady learning issue ends up critical in these applications [2] Many incremental methodologies were proposed for handling the problem of Associative Classifications such as Fast Update (FUP) [3], FUP2 [4], Insertion, Deletion and Updating [5], Galois Lattice theory [6], and New Fast Update (NFUP) [7]. Moreover, only a little attention was paid to problems in classification particularly in associative classification [8] and in the rule induction methods [9], researchers have paid small consideration to the incremental database issue.…”

Section: Introductionmentioning

confidence: 99%

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An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

Ramesh¹,

Saravanan²,

Manikandan³

2019

IJITEE

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Associative classification (AC) is an interesting approach in the domain of data mining which makes use of the association rules for building a classification system, which are easy for interpretation by the end user. The previous work [1] showed excellent performance in a static large data base but there existed a question of same performance when applied in an incremental data. Many of the Associative Classification methods have left the problem of data insertion and optimization unattended that results in serious performance degradation. To resolve this issue, we used new technique C-NTDI for building a classifier when there is an insertion of data that take place in a non-trivial fashion in the initial data that are used for updating the classification rules and thereafter to apply the PPCE technique for the generating of rules and further Proportion of Frequency occurrence count with BAT Algorithm (PFOCBA) is applied for optimizing the rules that are generated. The experiments were conducted on 6 different incremental data sets and we found that the proposed technique outperforms other methods such as ACIM, E-ACIM, Fast Update (FUP), Galois Lattice theory (GLT) and New Fast Update (NFUP) in terms of accuracy and time complexity.

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Section: Logarithmic Lossmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

Ramesh¹,

Saravanan²,

Manikandan³

2019

IJITEE

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“…A survey claims that di erent ARM algorithms show similar outputs despite being fundamentally di erent in terms of the employed strategy [2]. However, a study [8] shows that the itemset generation technique of Apriori is be er in extracting complete and correct rules. It also uses the search space reduction non-monotonicity principle for be er e ciency.…”

Section: Empirical Analysismentioning

confidence: 99%

Causal Connections Mining Within Security Event Logs

Khan

Parkinson

2017

Proceedings of the Knowledge Capture Conference

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Performing both security vulnerability assessment and con guration processes are heavily reliant on expert knowledge. is requirement o en results in many systems being le insecure due to a lack of analysis expertise and access to specialist resources. It has long been known that a system's event log provides historical information depicting potential security threats, as well as recording con guration activities. In this paper, a novel technique is developed that can process security event logs on a computer that has been assessed and con gured by a security professional, and autonomously establish causality amongst event log entries to learn performed con guration tasks. is extracted knowledge can then be exploited by non-professionals to plan steps that can improve the security of a previously unseen system.

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“…The designed FP-growth algorithm is then performed recursively by the produced conditional FP-tree to mine the frequent itemsets. Since FP-tree-like structure is more effective than the Apriori-like way for mining frequent itemsets or association rules, most approaches based on the FP-tree-like structure are still developed in progress [1,11,16,25].…”

Section: Fup Conceptmentioning

confidence: 99%

Updating high-utility pattern trees with transaction modification

Lin

Zhang

Gan

et al. 2014

Multimed Tools Appl

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Traditional association-rule mining only concerns the occurrence frequencies of the items in a binary database. In real-world applications, customers may buy several copies of the purchased items. Other factors such as profit, quantity, or price should be concerned to measure the utilities of the purchased items. High-utility itemsets mining was thus proposed to consider the factors of quantity and profit. Two-phase model was the most commonly way Multimed Tools Appl to keep the transaction-weighted utilization downward closure property, thus reducing the numerous candidates in utility mining. Most methods for finding high-utility itemsets are used to handle a static database. In practical applications, transactions are changed whether insertion, deletion, or modification. Some itemsets may arise as the new high-utility itemsets or become invalid knowledge in the updated database. In this paper, a maintenance Fast Updated High Utility Pattern tree for transaction MODification (FUP-HUP-tree-MOD) algorithm is thus proposed to effective maintain and update the built HUP tree for mining high-utility itemsets in dynamic databases without candidate generation. Experiments are conducted to show better performance of the proposed algorithm compared to the two-phase algorithm and the HUP tree algorithm in batch mode.

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Incremental association rule mining: a survey

Cited by 45 publications

References 53 publications

An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

An Optimized Associative Classifier for Incremental Data Based On Non-Trivial Data Insertion

Causal Connections Mining Within Security Event Logs

Updating high-utility pattern trees with transaction modification

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