Mining High Utility Quantitative Association Rules

Yen, Show‐Jane; Lee, Yue‐Shi

doi:10.1007/978-3-540-74553-2_26

Cited by 63 publications

(36 citation statements)

References 6 publications

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“…It requires only two scans in the database; this still requires memory and running time optimization. The authors in [27] have performed efficient mining of high utility item sets from large database by their proposed algorithm CTU-PROL. Again, this algorithm also suffers from large computation time and it requires appropriate improvement in the algorithm to reduce the computation duration.…”

Section: Related Workmentioning

confidence: 99%

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Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database

Kumar¹,

Thakur²

2017

IJIES

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High utility webpage set states to those set of webpages which have high utility value in a weblog database. To find high utility item sets from transactional databases, there exist various algorithms. However, these existing algorithms mostly deals with data like-categorical, time series, binary etc. The weblog data is different from other types of data. The utility webpages extracted from the log data can be used for knowing the user's behaviour. In this research paper, two algorithms named HUWSM (high utility webpage sets mining) and HUWP-FP (high utility webpage sets -frequent pattern) Tree have been developed and used for efficiently mining high utility webpage sets from web log database. Along with this, a pattern generation technique based on the 'Jaccard Similarity' is also included in this method. The HUWSM method has also been compared with various other existing methods. This algorithm has shown a much better performance and more effectiveness over other algorithms like FHM, HUI-MINER and IHUP methods in terms of memory consumption and execution time.

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Section: Related Workmentioning

confidence: 99%

“…For some webpage set X = {wp1, wp2 ....... wpn} is a webpage set where L is number of webpage set and it should be greater than or equal to 2, for all 2 ≤ n ≤ L. The Jaccard coefficient [27], which measures similarity between Patterns of webpage set P1 and P2 (say) can be calculated from the formula as given below -…”

Section: Def10mentioning

confidence: 99%

Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database

Kumar¹,

Thakur²

2017

IJIES

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“…The transaction utility and the external utility of an itemset was defined and general unified framework was developed to define a unifying view of the utility based measures for itemset mining. [16,25] In 2008 Alva Erwin1, Raj P. Gopalan, and N.R. Achuthan proposed Efficient Mining of High Utility Itemsets from Large Datasets High utility itemsets mining extends frequent pattern mining to discover itemsets in a transaction database with utility values above a given threshold.…”

Section: Related Workmentioning

confidence: 99%

Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items

Chaudhari¹,

Verma²

2013

IJCA

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Utility-based data mining is a new research area interested in all types of utility factors in data mining processes [1]. The basic meaning of utility is the quantity sold, interest, importance & profitability of items to the users. Utility of items in a transaction database consists of two aspects:1. The importance of distinct or unique items, which is called external utility. 2.The importance of the items in the transaction, w is called as internal utility.Mining high utility itemsets from the databases is not an easy task. Pruning search space for high utility itemset mining is difficult because a superset of a low utility itemset may be a high utility itemset. Existing studies [2,4,9] applied overestimated methods to facilitate the mining performance of utility mining. In these methods, first we will get potential high utility itemsets, and then an additional database scan is performed for identifying their utilities. However, the existing methods often generate a huge candidate itemsets and the mining performance is degraded consequently. In this paper we proposed Eliminating Unusual Itemset by Eliminating item set which is low utility item set to reduce search space. Proposed methods not only reduce the number of candidate itemsets, but also significantly increase the performance of the mining process.

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“…Association rule mining is considered to be an interesting research area and studied widely [1][2][3][4][5][6][7][8][9] by many researchers. In the recent years, some relevant methods have been proposed for mining high utility itemsets from transaction databases.…”

Section: Related Workmentioning

confidence: 99%

An Improved UP-Growth High Utility Itemset Mining

Adinarayanareddy¹,

Rao²,

Prasad³

2012

IJCA

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Efficient discovery of frequent itemsets in large datasets is a crucial task of data mining. In recent years, several approaches have been proposed for generating high utility patterns, they arise the problems of producing a large number of candidate itemsets for high utility itemsets and probably degrades mining performance in terms of speed and space. Recently proposed compact tree structure, viz., UP-Tree, maintains the information of transactions and itemsets, facilitate the mining performance and avoid scanning original database repeatedly. In this paper, UP-Tree (Utility Pattern Tree) is adopted, which scans database only twice to obtain candidate items and manage them in an efficient data structured way. Applying UP-Tree to the UP-Growth takes more execution time for Phase II. Hence this paper presents modified algorithm aiming to reduce the execution time by effectively identifying high utility itemsets.

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Mining High Utility Quantitative Association Rules

Cited by 63 publications

References 6 publications

Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database

Jaccard Similarity based Mining for High Utility Webpage Sets from Weblog Database

Mining High Utility Itemsets from Large Dynamic Dataset by Eliminating Unusual Items

An Improved UP-Growth High Utility Itemset Mining

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