Efficient constraint-based Sequential Pattern Mining (SPM) algorithm to understand customers’ buying behaviour from time stamp-based sequence dataset (original) (raw)
Related papers
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.
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.
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 2018
Today, the product sales at XYZ Bookstore are increase in accordance to the trend in society. In that case, high sales must be supported by good supply and on target. Product sold based on needs of consumers will make possibility to achieve high sales. Using the Sequential Pattern Mining approach, we can specify sales patterns of products in relation to another products. SPADE (Sequential Pattern Discovery using Equivalence classes) is an algorithm that can be used to find sequential patterns in a large database. This algorithm finds frequent sequences of the sales transaction data using database vertical and join process of the sequence. The results of SPADE algorithm is frequent sequences which are used to form the rules. Those can be used as predictors of other items that will be purchased by consumers in the future. The result of this study is a lot of unique sequence appears that can provide the best advice for Merchandiser Officer, for example, there are 1.468 sequences that p...
Mining Sequential Patterns: A Context-Aware Approach
Studies in Computational Intelligence, 2013
Traditional sequential patterns do not take into account contextual information associated with sequential data. For instance, when studying purchases of customers in a shop, a sequential pattern could be "frequently, customers buy products A and B at the same time, and then buy product C". Such a pattern does not consider the age, the gender or the socio-professional category of customers. However, by taking into account contextual information, a decision expert can adapt his/her strategy according to the type of customers. In this paper, we focus on the analysis of a given context (e.g., a category of customers) by extracting context-dependent sequential patterns within this context. For instance, given the context corresponding to young customers, we propose to mine patterns of the form "buying products A and B then product C is a general behavior in this population" or "buying products B and D is frequent for young customers only". We formally define such contextdependent sequential patterns and highlight relevant properties that lead to an efficient extraction algorithm. We conduct our experimental evaluation on real-world data and demonstrate performance issues.
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.
CRM Customer Value based on Constrained Sequential Pattern Mining
International Journal of Computer Applications, 2013
The role of data mining has become increasingly important for an organization that has large databases of information on customers. Customer Relationship Management (CRM) systems are implemented to identify the most profitable customers and manage the relationship of company with them. Intelligent data mining tools and techniques are used as backbone to the whole CRM initiative taken by the companies. Data mining tools search the data warehouse maintained by the companies and predict the hidden patterns and present them in form of a model. Strategic decisions about the customers can be taken based on the outcomes of these models. The data mining researchers have presented various mining algorithms to extract patterns in data for successful CRM approach. These approaches are however facing several problems like they are not business-focused and often results in enormous size of data after applying mining approaches. They have no relevant mechanism to provide guidance for focusing on specific category of customers for business profitability. In this article, the requirements of sequential pattern mining process for CRM environment is described, and then a novel constraint guided model for knowledge discovery process is proposed. We have suggested even how the selection of appropriate constraints can be made from the perspective of customer value analysis.
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.
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.
Comprehensive Study of Weighted Sequential Pattern Mining
2013
Extensive growth of data gives the motivation to find meaningful patterns among the huge data. Sequential pattern provides us interesting relationships between different items in sequential database. In the real world, there are several applications in which specific sequences are more important than other sequences. Traditional Sequential pattern approaches are suffering from two disadvantages: Firstly, all the items and sequences are treated uniformly. Second, conventional algorithms are generating large number of patterns for lower support. In addition, the unimportant patterns with low weights can be detected. This paper addresses problem of traditional framework and various framework of weighted sequential pattern. Paper also discuses how algorithm mines sequential pattern which reduces the search space and new pruning technique prune the unimportant pattern and pick only those patterns which leads to important and emerging pattern. Later section of paper discuses results of si...
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.