An Efficient Parallel High Utility Sequential Pattern Mining Algorithm

Zhang, Chunkai; Zu, Yiwen

doi:10.1109/hpcc/smartcity/dss.2019.00392

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Several existing studies have proposed some sequence pattern mining upper bounds. Three typical upper bounds: sequence extension utility (SEU) [22], sequence projected utilization (SPU) [29], and sequence-weighted utilization (SWU) [19] had been introduced compared in ref. [23].…”

Section: Upper Bounds and Pruning Strategiesmentioning

confidence: 99%

“…The main task of HUSPM is to discover high-utility sequential patterns (HUSPs), which are subsequences with high utility values. Researchers have proposed many efficient algorithms for mining HUSPs [22,23], such as designing efficient pruning strategies and incorporating novel data structures. Notably, compared to SPM and HUIM, HUSPM takes into account the chronological ordering of items and their associated utility values [24].…”

Section: Introductionmentioning

confidence: 99%

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Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

Cao,

Duan

2023

Applied Sciences

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High-utility sequential pattern mining (HUSPM) helps researchers find all subsequences that have high utility in a quantitative sequential database. The HUSPM approach appears to be well suited for resource transformation in DIKWP graphs. However, all the extensions of a high-utility sequential pattern (HUSP) also have a high utility that increases with its length. Therefore, it is difficult to obtain diverse patterns of resources. The patterns that consist of many low-utility items can also be a HUSP. In practice, such a long pattern is difficult to analyze. In addition, the low-utility items do not always reflect the interestingness of association rules. High average-utility pattern mining is considered a solution to extract more significant patterns by considering the lengths of patterns. In this paper, we formulate the problem of top-k high average-utility sequential pattern mining (HAUSPM) and propose a novel algorithm for resource transformation. We adopt a projection mechanism to improve efficiency. We also adopt the sequence average-utility-raising strategy to increase thresholds. We design the prefix extension average utility and the reduced sequence average utility by incorporating the average utility into the utility upper bounds. The results of our comparative experiments demonstrate that the proposed algorithm can achieve sufficiently good performance.

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Section: Upper Bounds and Pruning Strategiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

Cao,

Duan

2023

Applied Sciences

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“…Recently, for further performance improvement, several algorithms have been proposed for HUSPM. For example, HUS-UT [43] adopts an efficient data structure called a utility table to facilitate the utility calculation; a parallel version called HUS-Par was also proposed. In addition, the novel data structures of utility-array and UL-list were proposed for ProUM [20] and HUSP-ULL [21], respectively, to quickly discover HUSPs.…”

Section: High-utility Sequential Pattern Miningmentioning

confidence: 99%

“…Specifically, compared to the aforementioned SPM and HUIM, HUSPM considers the chronological ordering of items as well as utility values associated with them [21], which makes it a much more challenging and complex problem. Many researchers have proposed algorithms [20,41,43] with novel pruning strategies and data structures to efficiently mine HUSPs.…”

Section: Introductionmentioning

confidence: 99%

TKUS: Mining Top-K High-Utility Sequential Patterns

Zhang

Gan

et al. 2020

Preprint

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High-utility sequential pattern mining (HUSPM) has recently emerged as a focus of intense research interest. The main task of HUSPM is to find all subsequences, within a quantitative sequential database, that have high utility with respect to a user-defined minimum utility threshold. However, it is difficult to specify the minimum utility threshold, especially when database features, which are invisible in most cases, are not understood. To handle this problem, top-k HUSPM was proposed. Up to now, only very preliminary work has been conducted to capture top-k HUSPs, and existing strategies require improvement in terms of running time, memory consumption, unpromising candidate filtering, and scalability. Moreover, no systematic problem statement has been defined. In this paper, we formulate the problem of top-k HUSPM and propose a novel algorithm called TKUS. To improve efficiency, TKUS adopts a projection and local search mechanism and employs several schemes, including the Sequence Utility Raising, Terminate Descendants Early, and Eliminate Unpromising Items strategies, which allow it to greatly reduce the search space. Finally, experimental results demonstrate that TKUS can achieve sufficiently good top-k HUSPM performance compared to state-of-the-art algorithm TKHUS-Span.

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“…Identifying association rules from datasets of transactions, in which each transaction is comprised of a set of items, is one of the most important challenges in the field of data mining. Finding frequent item sets inside a huge database has been the subject of several different algorithmic proposals [15,17,19,20]. One of the most wellknown techniques for mining frequently used item sets in a transactional database is called the apriori algorithm [9].…”

Section: Introductionmentioning

confidence: 99%

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

Sujit R Wakchaure

2023

IJRITCC

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One of the most well-known and widely implemented data mining methods is Apriori algorithm which is responsible for mining frequent item sets. The effectiveness of the Apriori algorithm has been improved by a number of algorithms that have been introduced on both parallel and distributed platforms in recent years. They are distinct from one another on account of the method of load balancing, memory system, method of data degradation, and data layout that was utilised in their implementation. The majority of the issues that arise with distributed frameworks are associated with the operating costs of handling distributed systems and the absence of high-level parallel programming languages. In addition, when using grid computing, there is constantly a possibility that a node will fail, which will result in the task being re-executed multiple times. The MapReduce approach that was developed by Google can be used to solve these kinds of issues. MapReduce is a programming model that is applied to large-scale distributed processing of data on large clusters of commodity computers. It is effective, scalable, and easy to use. MapReduce is also utilised in cloud computing. This research paper presents an enhanced version of the Apriori algorithm, which is referred to as Improved Parallel and Distributed Apriori (IPDA). It is based on the scalable environment referred as Hadoop MapReduce, which was used to analyse Big Data. Through the generation of split-frequent data regionally and the early elimination of unusual data, the proposed work has its primary objective to reduce the enormous demands placed on available resources as well as the reduction of the overhead communication that occurs whenever frequent data are retrieved. The paper presents the results of tests, which demonstrate that the IPDA performs better than traditional apriori and parallel and distributed apriori in terms of the amount of time required, the number of rules created, and the various minimum support values.

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An Efficient Parallel High Utility Sequential Pattern Mining Algorithm

Cited by 8 publications

References 7 publications

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

Mining Top-k High Average-Utility Sequential Patterns for Resource Transformation

TKUS: Mining Top-K High-Utility Sequential Patterns

MR-AT: Map Reduce based Apriori Technique for Sequential Pattern Mining using Big Data in Hadoop

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