Mining Sequential Patterns: A Context-Aware Approach (original) (raw)
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Cogent engineering, 2015
Business Strategies are formulated based on an understanding of customer needs. This requires development of a strategy to understand customer behaviour and buying patterns, both current and future. This involves understanding, first how an organization currently understands customer needs and second predicting future trends to drive growth. This article focuses on purchase trend of customer, where timing of purchase is more important than association of item to be purchased, and which can be found out with Sequential Pattern Mining (SPM) methods. Conventional SPM algorithms worked purely on frequency identifying patterns that were more frequent but suffering from challenges like generation of huge number of uninteresting patterns, lack of user's interested patterns, rare item problem, etc. Article attempts a solution through development of a SPM algorithm based on various constraints like Gap, Compactness, Item, Recency, Profitability and Length along with Frequency constraint. Incorporation of six additional constraints is as well to ensure that all patterns are recently active (Recency), active for certain time span (Compactness), profitable and indicative of next timeline for purchase (Length-Item-Gap). The article also attempts to throw light on how proposed
A Survey on Different Approaches for Sequential Pattern Mining
In data mining, mining sequential pattern from very huge amount of database is very useful in many applications. Most of sequential pattern mining algorithms work on static data means the database should not change. But the databases in today’s real world application do not have static data, they are incremental databases. New transactions are added at some intervals of time. For updated database, the algorithm needs to be executed again for whole sequence database. So those approaches are not appropriate to use, for that algorithm with incremental approach should be modelled and used. This paper analysis existing approaches for finding sequential pattern mining, and the survey would be helpful in forming a new model or improving some existing approach to handle incremented database & obtain sequential patterns out of them.
The International Arab Journal of Information Technology, 2014
Sequential pattern mining is advantageous for several applications for example, it finds out the sequential purchasing behavior of majority customers from a large number of customer transactions. However, the existing researches in the field of discovering sequential patterns are based on the concept of frequency and presume that the customer purchasing behavior sequences do not fluctuate with change in time, purchasing cost and other parameters. To acclimate the sequential patterns to these changes, constraint are integrated with the traditional sequential pattern mining approach. It is possible to discover more user-centered patterns by integrating certain constraints with the sequential mining process. Thus in this paper, monetary and compactness constraints in addition to frequency and length are included in the sequential mining process for discovering pertinent sequential patterns from sequential databases. Also, a CFML-PrefixSpan algorithm is proposed by integrating these constraints with the original PrefixSpan algorithm, which allows discovering all CFML sequential patterns from the sequential database. The proposed CFML-PrefixSpan algorithm has been validated on synthetic sequential databases. The experimental results ensure that the efficacy of the sequential pattern mining process is further enhanced in view of the fact that the purchasing cost, time duration and length are integrated with the sequential pattern mining process.
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.
An Efficient Approach for Mining Sequential Pattern
Advances in Intelligent Systems and Computing, 2016
Sequential pattern mining (SPM) plays an important role in data mining, with broad applications such as in financial markets, education, medicine, and prediction. Although there are many efficient algorithms for SPM, the mining time is still high, especially for mining sequential patterns from huge databases, which require the use of a parallel technique. In this paper, we propose a parallel approach named MCM-SPADE (Multiple threads CM-SPADE), for use on a multi-core processor system as a multi-threading technique for SPM with very large database, to enhance the performance of the previous methods SPADE and CM-SPADE. The proposed algorithm uses the vertical data format and a data structure named CMAP (Co-occurrence MAP) for storing co-occurrence information. Based on the data structure CMAP, the proposed algorithm performs early pruning of the candidates to reduce the search space and it partitions the related tasks to each processor core by using the divide-andconquer property. The proposed algorithm also uses dynamic scheduling to avoid task idling and achieve load balancing between processor cores. The experimental results show that MCM-SPADE attains good parallelization efficiency on various input databases.
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys, 2013
Sequences of events, items or tokens occurring in an ordered metric space appear often in data and the requirement to detect and analyse frequent subsequences is a common problem. Sequential Pattern Mining arose as a sub-field of data mining to focus on this field. This paper surveys the approaches and algorithms proposed to date.
Target Oriented Sequential Pattern Mining using Recency and Monetary Constraints
International Journal of Computer Applications, 2012
Many approaches in constraint based sequential pattern mining have been proposed and most of them focus only on the concept of frequency, which means, if a pattern is not frequent, it is removed from further consideration. Frequency is a good indicator of the importance of a pattern but in real life, however, the environment may change constantly and patterns discovered from database may also change over time. Therefore, the users' recent behavior is not necessarily the same as the past ones and a pattern that occurs frequently in the past may never happen again in the future. So in this paper we have considered recency constraint to overcome this problem. Also we have considered one more constraint, monetary constraint since for making effective marketing strategies it is important to know the value of customer on the basis of what they are purchasing periodically and how much they are spending. So this motivates to consider monetary value of customers for targeting profitable customers. Along with that we have included the concept of mining only target oriented sequential patterns which satisfy RFM constraints to find the happening order of a concerned itemsets only, for taking effective marketing decisions.
Margin-closed frequent sequential pattern mining
Proceedings of the ACM SIGKDD Workshop on Useful Patterns - UP '10, 2010
We present a new approach to mining sequential patterns that significantly reduces the number of patterns reported, favoring longer patterns and suppressing shorter patterns with similar frequencies. This is achieved by mining only margin-closed patterns whose support differs by more than some margin from any extension. Our approach extends the efficient BIDE algorithm to enforce the margin constraint. The set of margin-closed patterns can be significantly smaller than a set of just closed patterns while retaining the most important information about the dataset. This is shown by an extensive empirical evaluation on six real life databases.
A Comprehensive Survey of Pattern Mining: Challenges and Opportunities
International Journal of Computer Applications, 2018
Pattern mining is an important field of data mining. The fundamental task of data mining is to explore the database to find out sequential, frequent patterns. In recent years, data mining has shifted its focus to design methods for discovering patterns with user expectations. In this regard various types of pattern mining methods have been proposed. Frequent pattern mining, sequential pattern mining, temporal pattern mining, and constraint based pattern mining. Pattern mining has various useful real-life applications such as market basket analysis, e-learning, social network analysis, web page, click sequences, Bioinformatics, etc., this paper presents a survey of various types of pattern mining. The main goal of this paper is to present both an introduction to all pattern mining and a survey of various algorithms, challenges and research opportunities. This paper not only discusses the problems of pattern mining and its related applications, but also the extensions and possible future improvements in this field.
Mining Sequential Pattern with Time-Constraint
2020
Abstract-Sequential pattern mining is an important data mining task, and different algorithms have been proposed to perform this task efficiently. The problem is to find all sequential patterns with higher or equal support to a predefined minimum support threshold in a data sequence database. Here we present a new methodology to mine a sequential pattern with time-constraint. Our study shows that constraints can be effectively and efficiently pushed deep into sequential pattern mining under this new framework.