Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

Tseng, Vincent S.; Wu, Chengwei; Fournier-Viger, Philippe; Yu, Philip S.

doi:10.1109/tkde.2014.2345377

Cited by 107 publications

(72 citation statements)

References 26 publications

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“…The source code of all algorithms and datasets used in the experiments can be downloaded as part of the SPMF data mining library goo.gl/rIKIub [3]. For future work, we will extend EFIM for popular variations of the HUIM problem such as mining closed+ high-utility itemset [13], generators of high-utility itemsets [5], and on-shelf high-utility itemsets [4].…”

Section: Resultsmentioning

confidence: 99%

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

Zida

Fournier-Viger

Lin

et al. 2015

Lecture Notes in Computer Science

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123

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Abstract. High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms d2 HUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.

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Section: Resultsmentioning

confidence: 99%

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

Zida

Fournier-Viger

Lin

et al. 2015

Lecture Notes in Computer Science

Self Cite

123

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“…Foodmart and Chainstore already contain unit profits and purchase quantities. For other datasets, unit profits of items are generated between 1 and 1,000 by using a log-normal distribution and quantities of items are generated randomly between 1 and 5, as the settings of [25,26,27]. Synthetic datasets were generated from the data generator in [1].…”

Section: Performance Evaluationmentioning

confidence: 99%

Efficient Algorithms for Mining Top-K High Utility Itemsets

Tseng¹,

Wu²,

Fournier-Viger

et al. 2016

IEEE Trans. Knowl. Data Eng.

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221

110

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High utility itemsets (HUIs) mining is an emerging topic in data mining, which refers to discovering all itemsets having a utility meeting a user-specified minimum utility threshold min_util. However, setting min_util appropriately is a difficult problem for users. Generally speaking, finding an appropriate minimum utility threshold by trial and error is a tedious process for users. If min_util is set too low, too many HUIs will be generated, which may cause the mining process to be very inefficient. On the other hand, if min_util is set too high, it is likely that no HUIs will be found. In this paper, we address the above issues by proposing a new framework for top-k high utility itemset mining, where k is the desired number of HUIs to be mined. Two types of efficient algorithms named TKU (mining Top-K Utility itemsets) and TKO (mining Top-K utility itemsets in One phase) are proposed for mining such itemsets without the need to set min_util. We provide a structural comparison of the two algorithms with discussions on their advantages and limitations. Empirical evaluations on both real and synthetic datasets show that the performance of the proposed algorithms is close to that of the optimal case of state-of-the-art utility mining algorithms.

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“…'U mining' still suffers from this above shortcoming in the algorithm. Moreover, according to [14], a novel algorithm for mining high utility item sets has been proposed. This algorithm is still time and memory consuming.…”

Section: Related Workmentioning

confidence: 99%

“…number of times the webpage is referenced in a session) and external utility consists of impor¬tance of distinct item [12,13]. A webpage is of high utility [14,15] if its utility is greater than the minimum specified utility value. The proposed method HUWSM focuses on magnitude of utility value rather than quantity of webpage sets.…”

Section: Introductionmentioning

confidence: 99%

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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Efficient Algorithms for Mining the Concise and Lossless Representation of High Utility Itemsets

Cited by 107 publications

References 26 publications

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

Efficient Algorithms for Mining Top-K High Utility Itemsets

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

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