An efficient algorithm for mining periodic high-utility sequential patterns

Dinh, Duy-Tai; Le, Bac; Fournier-Viger, Philippe; Huynh, Van–Nam

doi:10.1007/s10489-018-1227-x

Cited by 42 publications

(15 citation statements)

References 45 publications

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“…We plan to extend this approach for mixed numeric and categorical data clustering, as well as parallel methods for clustering large-scale data sets. In addition, we have some ideas to adapt the categorical data clustering framework to the topics of high-utility sequential pattern mining/hiding [19,20,38,39].…”

Section: Discussionmentioning

confidence: 99%

“…From (19), if x i and Z l contain identical categories at each attribute or Z l contains only x i , then the dissimilarity between them is zero. If categories at each attribute of x i and Z l are totally different, then the dissimilarity between them equals to the number of features.…”

Section: Definition 9 (Dissimilarity Between Objects and Cluster Centers)mentioning

confidence: 99%

“…After forming k initial clusters, k-PbC defines centers for these clusters by using (13). For each data object in D, k-PbC calculates the distance between that object and k clusters by using the (19) and then assigns it to the nearest cluster (line 4). In the next step, cluster centers are updated by using (13) (lines 5).…”

Section: The K-pbc Algorithmmentioning

confidence: 99%

“…This information is then used to perform the maximal frequent itemset mining algorithm. In addition, the procedure also tracks all transaction IDs containing each item (lines [12][13][14][15][16][17][18][19]. This information is latter used for the matching step.…”

Section: The K-pbc Algorithmmentioning

confidence: 99%

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k-PbC: an improved cluster center initialization for categorical data clustering

Dinh

Huynh

2020

Appl Intell

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The performance of a partitional clustering algorithm is influenced by the initial random choice of cluster centers. Different runs of the clustering algorithm on the same data set often yield different results. This paper addresses that challenge by proposing an algorithm named k-PbC, which takes advantage of non-random initialization from the view of pattern mining to improve clustering quality. Specifically, k-PbC first performs a maximal frequent itemset mining approach to find a set of initial clusters. It then uses a kernel-based method to form cluster centers and an information-theoretic based dissimilarity measure to estimate the distance between cluster centers and data objects. An extensive experimental study was performed on various real categorical data sets to draw a comparison between k-PbC and state-of-the-art categorical clustering algorithms in terms of clustering quality. Comparative results have revealed that the proposed initialization method can enhance clustering results and k-PbC outperforms compared algorithms for both internal and external validation metrics.

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Section: Discussionmentioning

confidence: 99%

Section: Definition 9 (Dissimilarity Between Objects and Cluster Centers)mentioning

confidence: 99%

Section: The K-pbc Algorithmmentioning

confidence: 99%

Section: The K-pbc Algorithmmentioning

confidence: 99%

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k-PbC: an improved cluster center initialization for categorical data clustering

Dinh

Huynh

2020

Appl Intell

Self Cite

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“…Recently, some interesting issues of HUSPM have been extensively studied that can improve the effectiveness of mining high-utility sequential patterns. For example, the problems of mining top-k high-utility sequential patterns [45,41], discovering periodic HUSPs [10], mining HUSPs with multiple minimum utility thresholds [28], and incrementally mining HUSPs on a dynamic database [40] have been addressed. It should be noted that several genetic algorithms have been developed for HUIM (e.g., HUIM-BPSO [27] and HUIM-ACS [43]), but they have not been proposed to deal with HUSPM yet.…”

Section: Utility-driven Mining On Sequencesmentioning

confidence: 99%

ProUM: Projection-based utility mining on sequence data

Gan

Lin

Zhang

et al. 2020

Information Sciences

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Utility is an important concept in economics. A variety of applications consider utility in real-life situations, which has lead to the emergence of utility-oriented mining (also called utility mining) in the recent decade. Utility mining has attracted a great amount of attention, but most of the existing studies have been developed to deal with itemset-based data. Time-ordered sequence data is more commonly seen in real-world situations, which is different from itemset-based data. Since they are time-consuming and require large amount of memory usage, current utility mining algorithms still have limitations when dealing with sequence data. In addition, the mining efficiency of utility mining on sequence data still needs to be improved, especially for long sequences or when there is a low minimum utility threshold. In this paper, we propose an efficient Projection-based Utility Mining (ProUM) approach to discover high-utility sequential patterns from sequence data. The utility-array structure is designed to store the necessary information of the sequence-order and utility. ProUM can significantly improve the mining efficiency by utilizing the projection technique in generating utility-array, and it effectively reduces the memory consumption. Furthermore, a new upper bound named sequence extension utility is proposed and several pruning strategies are further applied to improve the efficiency of ProUM. By taking utility theory into account, the derived high-utility sequential patterns have more insightful and interesting information than other kinds of patterns. Experimental results showed that the proposed ProUM algorithm significantly outperformed the state-of-the-art algorithms in terms of execution time, memory usage, and scalability.

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Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Ryu

Yun

Lee

et al. 2022

Int J of Intelligent Sys

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Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. In addition, utility occupancy pattern mining, which considers the quantity, importance, and proportion of the pattern in the transaction, has been proposed. Despite this advantage, there is no utility seizing approach to handle the dynamically generated data flows. As electronics are interconnected and intelligent systems are constructed, data is generated in real‐time and accumulated rapidly. Therefore, a method to read data immediately in a dynamic environment and efficiently analyze massive data is required. To overcome the limitations of the existing utility occupancy methods, we propose a novel mining approach, HUOMI, which performs quickly on an increasing database. The suggested algorithm has an optimized data structure and an improved pruning technique, which can respond to the dynamic environment promptly. To indicate the effectiveness of the proposed method, performance evaluations were conducted on real and synthetic data sets. In the experimental results, the suggested algorithm showed a better performance than the other state‐of‐the‐art algorithms.

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An efficient algorithm for mining periodic high-utility sequential patterns

Cited by 42 publications

References 45 publications

k-PbC: an improved cluster center initialization for categorical data clustering

k-PbC: an improved cluster center initialization for categorical data clustering

ProUM: Projection-based utility mining on sequence data

Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

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