ExMiner: An Efficient Algorithm for Mining Top-K Frequent Patterns

Quang, Tran; Oyanagi, Shigeru; Yamazaki, Katsuhiro

doi:10.1007/11811305_48

Cited by 18 publications

(11 citation statements)

References 3 publications

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“…Many studies have been proposed to mine different kinds of top-k patterns, such as top-k frequent itemsets [3,19,20], top-k frequent closed itemsets [3,28], top-k closed sequential patterns [24], top-k association rules [6], top-k sequential rules [5], top-k correlation patterns [31,32,33] and top-k cosine similarity interesting pairs [38]. What distinguishes each top-k pattern mining algorithm is the type of patterns discovered, as well as the data structures and search strategies that are employed.…”

Section: Top-k Pattern Miningmentioning

confidence: 99%

“…What distinguishes each top-k pattern mining algorithm is the type of patterns discovered, as well as the data structures and search strategies that are employed. For example, some algorithms [5,6] use a rule expansion strategy for finding patterns, while others rely on a pattern-growth search using structures such as FP-Tree [19,20,28]. The choice of data structures and search strategy affect the efficiency of a top-k pattern mining algorithm in terms of both memory and execution time.…”

Section: Top-k Pattern Miningmentioning

confidence: 99%

“…REQUENT itemset mining (FIM) [1,3,8,9,18,19,20,28,29] is a fundamental research topic in data mining. However, the traditional FIM may discover a large amount of frequent but low-value itemsets and lose the information on valuable itemsets having low selling frequencies.…”

Section: Introductionmentioning

confidence: 99%

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Efficient Algorithms for Mining Top-K High Utility Itemsets

Tseng¹,

Wu²,

Fournier-Viger

et al. 2016

IEEE Trans. Knowl. Data Eng.

230

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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Section: Top-k Pattern Miningmentioning

confidence: 99%

Section: Top-k Pattern Miningmentioning

confidence: 99%

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Efficient Algorithms for Mining Top-K High Utility Itemsets

Tseng¹,

Wu²,

Fournier-Viger

et al. 2016

IEEE Trans. Knowl. Data Eng.

230

110

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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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“…A similar problem occurring in FIM is how to determine an appropriate minimum support threshold to mine enough but not too many itemsets for the users. To precisely control the output size and discover the most frequent patterns without setting the threshold, a good solution is to change the task of mining frequent patterns to the task of mining the top-k frequent patterns [4,5,7,9,10,14,16,17,22]. The idea is to let the users specify k, i.e., the number of desired patterns, instead of specifying the minimum support threshold.…”

Section: Introductionmentioning

confidence: 99%

Mining top-K high utility itemsets

Shie

Tseng

et al. 2012

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

107

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Mining high utility itemsets from databases is an emerging topic in data mining, which refers to the discovery of itemsets with utilities higher than a user-specified minimum utility threshold min_util. Although several studies have been carried out on this topic, setting an appropriate minimum utility threshold is a difficult problem for users. If min_util is set too low, too many high utility itemsets will be generated, which may cause the mining algorithms to become inefficient or even run out of memory. On the other hand, if min_util is set too high, no high utility itemset will be found. Setting appropriate minimum utility thresholds by trial and error is a tedious process for users. In this paper, we address this problem by proposing a new framework named top-k high utility itemset mining, where k is the desired number of high utility itemsets to be mined. An efficient algorithm named TKU (Top-K Utility itemsets mining) is proposed for mining such itemsets without setting min_util. Several features were designed in TKU to solve the new challenges raised in this problem, like the absence of anti-monotone property and the requirement of lossless results. Moreover, TKU incorporates several novel strategies for pruning the search space to achieve high efficiency. Results on real and synthetic datasets show that TKU has excellent performance and scalability.

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ExMiner: An Efficient Algorithm for Mining Top-K Frequent Patterns

Cited by 18 publications

References 3 publications

Efficient Algorithms for Mining Top-K High Utility Itemsets

Efficient Algorithms for Mining Top-K High Utility Itemsets

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

Mining top-K high utility itemsets

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