A weighted frequent itemset mining algorithm for intelligent decision in smart systems

Zhao, Xuejian; Zhang, Xinhui; Wang, Pan; Chen, Songle

doi:10.1109/access.2018.2839751

Cited by 19 publications

(15 citation statements)

References 27 publications

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“…WIM To address the FIM limitation, WIM is introduced, where weights are associated to each item to indicate their relative importance in the given transaction [24]. The goal is to extract itemsets exceeding minimum weight threshold.…”

Section: Related Workmentioning

confidence: 99%

A general-purpose distributed pattern mining system

et al. 2020

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This paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern mining problems by reducing the enumeration search space. Thus, it derives the relevant patterns by studying the different correlation among the transactions. It first decomposes the set of transactions into several clusters of different sizes, and then explores heterogeneous architectures, including MapReduce, single CPU, and multi CPU, based on the densities of each subset of transactions. To evaluate the DT-DPM framework, extensive experiments were carried out by solving five pattern mining problems (FIM: Frequent Itemset Mining, WIM: Weighted Itemset Mining, UIM: Uncertain Itemset Mining, HUIM: High Utility Itemset Mining, and SPM: Sequential Pattern Mining). Experimental results reveal that by using DT-DPM, the scalability of the pattern mining algorithms was improved on large databases. Results also reveal that DT-DPM outperforms the baseline parallel pattern mining algorithms on big databases.

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Section: Related Workmentioning

confidence: 99%

A general-purpose distributed pattern mining system

et al. 2020

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“…To make comparison, we extend the two anti-monotonicity properties HEWI (Lin et al, 2016) and WD (Zhao et al, 2018) designed for expected-support w-FIs to w-PFI mining as the baseline methods, named p-HEWI and p-WD. For our proposed w-PFI mining Algorithm 1, we have designed three pruning methods in Corollary 1, 2 and 3.…”

Section: Algorithm 3 W-pfi Candidate Generation and Pruning Based On A Probability Modelmentioning

confidence: 99%

“…To handle it, Lin, Gan, Fournier‐Viger, Hong, and Tseng (2016) make an attempt to maintain the anti‐monotonicity via the maximal weight of all items, named high upper‐bound expected weighted downward closure (HEWI). Noticing that HEWI may generate too many candidates, Zhao, Zhang, Pan, Chen, and Sun (2018) present a weight judgement downward closure property (WD) to early prune the search space and improve the time efficiency. However, the current studies on w‐FI mining over uncertain databases still suffer from the following two limitations: There are two kinds of FIs in uncertain databases as mentioned.…”

Section: Introductionmentioning

confidence: 99%

Efficient weighted probabilistic frequent itemset mining in uncertain databases

Chen

et al. 2020

Expert Systems

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Uncertain data mining has attracted so much interest in many emerging applications over the past decade. An issue of particular interest is to discover the frequent itemsets in uncertain databases. As an item would not appear in a transaction of such database for certain, several probability models are presented to measure the frequency of an itemset, and the frequent itemset over probabilistic data generally has two different definitions: the expected support-based frequent itemset and probabilistic frequent itemset. Meanwhile, it is noted that the frequency itself cannot identify useful or meaningful patterns in some scenarios. Other measures such as the importance of items should be also taken into account. To this end, some studies recently have been done on weighted (importance) frequent itemset mining in uncertain databases. However, they are only designed for the expected support-based frequent itemset, and suffer from low efficiency due to generating too many frequent itemset candidates. To address this issue, we propose a novel weighted probabilistic frequent itemsets (w-PFIs) algorithm. Moreover, we derive a probability model for the support of a w-PFI candidate in our method and present three pruning techniques to narrow the search space and remove the unpromising candidates immediately. Extensive experiments have been conducted on both real and synthetic datasets, to evaluate the performance of our w-PFI algorithm in terms of runtime, accuracy and scalability.Results show that our algorithm yields the best performance among the existing algorithms. K E Y W O R D S probability model, pruning, uncertain database, weighted probabilistic frequent itemset 1 | INTRODUCTION Recently, we have witnessed a rapid growth of uncertain data in emerging applications such as online market analysis, Radio Frequency Identificationbased monitoring, traffic data analysis and location-based service (

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“…Designed and implemented FiDoop-HD an extension of FiDoop that efficiently handles high-dimensional data processing. The weight judgment downward closure property for the weighted frequent itemsets and the existence property of weighted frequent subsets are introduced and proved first by the paper [20]. Based on these two properties, the Weight judgment downward closure property-based FIM (WD-FIM) algorithm is proposed to narrow the searching space of the weighted frequent itemsets and improve the time efficiency.…”

Section: IImentioning

confidence: 99%

Frequent Itemset Generation for Analyzing Customer Buying Nature using Bit Vector Mining

Harini¹

2019

IJRASET

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The paper introduces an efficient algorithm using bit vector to find frequent itemsets from a huge set of itemsets. The existing frequent itemset generation algorithms lack efficiency in terms of time and space. As a result, the need for new frequent itemset mining algorithms that could tackle the new trends are required. Here, the purchased items in a transaction are represented in terms of bit vectors. A bit vector is a vector in which each element is represented in terms of bits (so its value is either 0 or 1). The database is scanned only once. The k-frequent itemsets are obtained by performing LOGICAL AND operation between the items in the scanned table. By doing so, the time and space consumed in determining the frequent itemsets is reduced.

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A weighted frequent itemset mining algorithm for intelligent decision in smart systems

Cited by 19 publications

References 27 publications

A general-purpose distributed pattern mining system

A general-purpose distributed pattern mining system

Efficient weighted probabilistic frequent itemset mining in uncertain databases

Frequent Itemset Generation for Analyzing Customer Buying Nature using Bit Vector Mining

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