Distributed and parallel high utility sequential pattern mining (original) (raw)

Efficiently mining high utility sequential patterns in static and streaming data

Intelligent Data Analysis, 2017

High utility sequential pattern (HUSP) mining has emerged as a novel topic in data mining. Although some preliminary works have been conducted on this topic, they incur the problem of producing a large search space for high utility sequential patterns. In addition, they mainly focus on mining HUSPs in static databases and do not take streaming data into account, where unbounded data come continuously and often at a high speed. To efficiently deal with both problems, we propose a novel framework for mining high utility sequential patterns over static and streaming databases. In this regard, two efficient data structures named ItemUtilLists (Item Utility Lists) and HUSP-Tree (High Utility Sequential Pattern Tree) are proposed to maintain essential information for mining HUSPs in both offline and online fashions. In addition, a novel utility model called Sequence-Suffix Utility is proposed for effectively pruning the search space in HUSP mining. We propose an algorithm named HUSP-Miner (High Utility Sequential Pattern Miner) to find HUSPs in static databases efficiently. Then, a one-pass algorithm named HUSP-Stream (High Utility Sequential Pattern mining over Data Streams) is proposed to incrementally update ItemUtilLists and HUSP-Tree online and find HUSPs over data streams. To the best of our knowledge, HUSP-Stream is the first method to find HUSPs over data streams. Experimental results on both real and synthetic datasets show that HUSP-Miner outperforms the compared algorithms substantially in terms of execution time, memory usage and number of generated candidates. The experiments also demonstrate impressive performance of HUSP-Stream to update the data structures and discover HUSPs over data streams.

IJERT-A Parallel Approach For High Utility Patterns Mining From Distributed Databases

International Journal of Engineering Research and Technology (IJERT), 2012

https://www.ijert.org/a-parallel-approach-for-high-utility-patterns-mining-from-distributed-databases https://www.ijert.org/research/a-parallel-approach-for-high-utility-patterns-mining-from-distributed-databases-IJERTV1IS8564.pdf In recent years, the problem of high utility pattern mining become one of the most important research area in data mining. Traditional pattern mining algorithms may not find some most profitable, high priced patterns, due to their lower support. These algorithms reflect only statistical correlation, but it does not reflect semantic significance of the pattern. This gives reason to develop a mining model to find itemsets, which contributes to business organization with high profit. Hence, utility-based pattern mining technique has evolved and got much popularity in recent time. But all of the existing utility pattern mining algorithms are based on centralized database and today's internet era databases are inherently distributed. This inherent distribution source of data and the voluminous in size emerges to develop scalable parallel and distributed algorithm for pattern mining. This paper proposed a parallel and distributed method for mining high utility patterns and also prune the irrelevant data or items. This method is designed in such a way so that it can efficiently generate high utility itemsets with less execution time in distributed environment.

A Survey of Parallel Sequential Pattern Mining

ACM Transactions on Knowledge Discovery from Data

With the growing popularity of shared resources, large volumes of complex data of different types are collected automatically. Traditional data mining algorithms generally have problems and challenges including huge memory cost, low processing speed, and inadequate hard disk space. As a fundamental task of data mining, sequential pattern mining (SPM) is used in a wide variety of real-life applications. However, it is more complex and challenging than other pattern mining tasks, i.e., frequent itemset mining and association rule mining, and also suffers from the above challenges when handling the large-scale data. To solve these problems, mining sequential patterns in a parallel or distributed computing environment has emerged as an important issue with many applications. In this article, an in-depth survey of the current status of parallel SPM (PSPM) is investigated and provided, including detailed categorization of traditional serial SPM approaches, and state-of-the art PSPM. We re...

A Survey of High Utility Sequential Pattern Mining

Studies in Big Data, 2019

The problem of mining high utility sequences aims at discovering subsequences having a high utility (importance) in a quantitative sequential database. This problem is a natural generalization of several other pattern mining problems such as discovering frequent itemsets in transaction databases, frequent sequences in sequential databases, and high utility itemsets in quantitative transaction databases. To extract high utility sequences from a quantitative sequential database, the sequential ordering between items and their utility (in terms of criteria such as purchase quantities and unit profits) are considered. High utility sequence mining has been applied in numerous applications. It is much more challenging than the aforementioned problems due to the combinatorial explosion of the search space when considering sequences, and because the utility measure of sequences does not satisfy the downward-closure property used in pattern mining to reduce the search space. This chapter introduces the problem of high utility sequence mining, the state-of-art algorithms, applications, present related problems and research opportunities. A key contribution of the chapter is to also provide a theoretical framework for comparing upper-bounds used by high utility sequence mining algorithms. In particular, an interesting result is that an upper-bound used by the popular USpan algorithm is not an upper-bound. The consequence is that USpan is an incomplete algorithm, and potentially other algorithms extending USpan.

A Parallel Approach For High Utility Patterns Mining From Distributed Databases

International journal of engineering research and technology, 2012

In recent years, the problem of high utility pattern mining become one of the most important research area in data mining. Traditional pattern mining algorithms may not find some most profitable, high priced patterns, due to their lower support. These algorithms reflect only statistical correlation, but it does not reflect semantic significance of the pattern. This gives reason to develop a mining model to find itemsets, which contributes to business organization with high profit. Hence, utility-based pattern mining technique has evolved and got much popularity in recent time. But all of the existing utility pattern mining algorithms are based on centralized database and today's internet era databases are inherently distributed. This inherent distribution source of data and the voluminous in size emerges to develop scalable parallel and distributed algorithm for pattern mining. This paper proposed a parallel and distributed method for mining high utility patterns and also prune the irrelevant data or items. This method is designed in such a way so that it can efficiently generate high utility itemsets with less execution time in distributed environment.

Memory-adaptive high utility sequential pattern mining over data streams

Machine Learning, 2017

High utility sequential pattern (HUSP) mining has emerged as an important topic in data mining. A number of studies have been conducted on mining HUSPs, but they are mainly intended for non-streaming data and thus do not take data stream characteristics into consideration. Streaming data are fast changing, continuously generated unbounded in quantity. Such data can easily exhaust computer resources (e.g., memory) unless a proper resource-aware mining is performed. In this study, we explore the fundamental problem of how limited memory can be best utilized to produce high quality HUSPs over a data stream. We design an approximation algorithm, called MAHUSP, that employs memory adaptive mechanisms to use a bounded portion of memory, in order to efficiently discover HUSPs over data streams. An efficient tree structure, called MAS-Tree, is proposed to store potential HUSPs over a data stream. MAHUSP guarantees that all HUSPs are discovered in certain circumstances. Our experimental study shows that our algorithm can not only discover HUSPs over data streams efficiently, but also adapt to memory allocation with limited sacrifices in the quality of discovered HUSPs. Furthermore, in order to show the effectiveness and efficiency of MAHUSP in real-life applications, we apply our proposed algorithm to a web clickstream dataset obtained from a Canadian news portal to showcase users' reading behavior, and to a real biosequence database to identify disease-related gene regulation sequential patterns. The results show that MAHUSP effectively discovers useful and meaningful patterns in both cases.

Parallel mining of closed sequential patterns

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, 2005

Discovery of sequential patterns is an essential data mining task with broad applications. Among several variations of sequential patterns, closed sequential pattern is the most useful one since it retains all the information of the complete pattern set but is often much more compact than it. Unfortunately, there is no parallel closed sequential pattern mining method proposed yet. In this paper we develop an algorithm, called Par-CSP (Parallel Closed Sequential Pattern mining), to conduct parallel mining of closed sequential patterns on a distributed memory system. Par-CSP partitions the work among the processors by exploiting the divide-and-conquer property so that the overhead of interprocessor communication is minimized. Par-CSP applies dynamic scheduling to avoid processor idling. Moreover, it employs a technique, called selective sampling, to address the load imbalance problem. We implement Par-CSP using MPI on a 64-node Linux cluster. Our experimental results show that Par-CSP attains good parallelization efficiencies on various input datasets.

Distributed Mining of High Utility Sequential Patterns with Negative Item Values

2021

The sequential pattern mining was widely used to solve various business problems, including frequent user click pattern, customer analysis of buying product, gene microarray data analysis, etc. Many studies were going on these pattern mining to extract insightful data. All the studies were mostly concentrated on high utility sequential pattern mining (HUSP) with positive values without a distributed approach. All the ex-isting solutions are centralized which incurs greater computation and communication costs. In this paper, we introduce a novel algorithm for mining HUSPs including negative item values in support of a distributed approach. We use the Hadoop map reduce algorithms for processing the data in parallel. Various pruning techniques have been proposed to minimize the search space in a distributed environment, thus reducing the expense of processing. To our understanding, no algorithm was proposed to mine High Utility Sequential Patterns with negative item values in a distrib...

Mining High Utility Time Interval Sequences Using MapReduce Approach: Multiple Utility Framework

IEEE Access

Mining high utility sequential patterns is observed to be a significant research in data mining. Several methods mine the sequential patterns while taking utility values into consideration. The patterns of this type can determine the order in which items were purchased, but not the time interval between them. The time interval among items is important for predicting the most useful real-world circumstances, including retail market basket data analysis, stock market fluctuations, DNA sequence analysis, and so on. There are a very few algorithms for mining sequential patterns those consider both the utility and time interval. However, they assume the same threshold for each item, maintaining the same unit profit. Moreover, with the rapid growth in data, the traditional algorithms cannot handle the big data and are not scalable. To handle this problem, we propose a distributed three phase MapReduce framework that considers multiple utilities and suitable for handling big data. The time constraints are pushed into the algorithm instead of pre-defined intervals. Also, the proposed upper bound minimizes the number of candidate patterns during the mining process. The approach has been tested and the experimental results show its efficiency in terms of run time, memory utilization, and scalability. INDEX TERMS Data mining, MapReduce framework, multiple utility thresholds, sequential pattern mining, time interval patterns.

Survey on Approaches for Sequential Pattern Mining and High Utility Sequential Pattern Mining

International Journal For Scientific Research and Development, 2015

Sequential pattern mining plays an important role in many applications, such as bioinformatics and consumer behaviour analysis. However, the classic frequency-based framework often leads to many patterns being identified, most of which are not informative enough for business decision-making. So a recent effort has been to incorporate utility into the sequential pattern selection framework, so that high utility (frequent or infrequent) sequential patterns are mined which address typical business concerns such as dollar value associated with each pattern. So this paper presents detailed different approaches adopted for Sequential pattern mining algorithms as well as high utility sequential pattern mining techniques.