Sequential pattern mining has been used to predict various aspects of customer buying behavior for a long time. Discovered sequence reveals the chronological relation between items and provides valuable information to aid in developing marketing strategies. Nevertheless, we can hardly know whether the buying is cyclic and how long the interval between the two consecutive items in the sequential pattern is. To solve this problem, in this paper, data mining skills and the fundamentals of statistics are combined to develop a set of algorithms to unearth the cyclic properties of discovered sequential patterns. The algorithms, coupled with the sequential pattern mining process, constitute a thorough scheme to analyze and predict likely consumer behavior. The proposed algorithms are implemented and applied to test against real data collected from a consumer goods company. The experimental results illustrate how the model can be used to predict likely purchases within a certain time frame. Consequently, marketing professionals can execute campaigns to favorably impact customers' behaviors.

The main task of mining sequential patterns is to analyze the transaction database of a company in order to find out the priorities of items that most customers take when consuming. In this article, we propose a new method—the ISP Algorithm. With this method, we can find out not only the order of consumer items of each customer, but also offer the periodic interval of consumer items of each customer. Compared with other previous periodic association rules, the difference is that the period the algorithm provides is not the repeated purchases in a regular time, but the possible repurchases within a certain time frame. The algorithm utilizes the transaction time interval of individual customers and that of all the customers to find out when and who will buy goods, and what items of goods they will buy. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 359–373, 2005.

We propose a fuzzy linguistic prediction method for improving fuzzy random prediction. It uses the bit map summary to determine the trend of objects rising or falling and then uses the fuzzy linguistic summary to compute the parameter of fuzzy random prediction. To show how this method works, we use it to predict the present Taiwan leading index, which gives the economic trend for the next 3 months. By further predicting the leading indices of months that follow, we can forecast the economic trend for the next year. © 2003 Wiley Periodicals, Inc.

In this paper, we develop a new method to measure the quality of each tuple as an answer with respect to Select-Project-Join (SPJ) queries so that we can determine which answers are better answers to the given query in a fuzzy relational database. The quality of an answer is viewed as how much sure information is provided, and how much extra information is needed so that it will be a sure answer to the query. The less extra information that is required and the more sure information that is provided by an answer, the higher the quality of that answer is, and in consequence, it will be more reliable. © 2001 John Wiley & Sons, Inc.

A multituning fuzzy control system structure that involves two simple, but effective tuning mechanisms, is proposed: one is called fuzzy control rule tuning mechanism (FCRTM); the other is called dynamic scalar tuning mechanism (DSTM). In FCRTM, it is used to generate the necessary control rules with a center extension method. In DSTM, it contains three fuzzy IF-THEN rules for determining the appropriate scaling factors for the fuzzy control system. In this paper, a method based on the genetic algorithm (GA) is proposed to simultaneously choose the appropriate parameters in FCRTM and DSTM. That is, the proposed GA-based method can automatically generate the required rule base of fuzzy controller and efficiently determine the appropriate map for building the dynamic scalars of fuzzy controller. A multiobjective fitness function is proposed to determine an appropriate parameter set such that not only the selected fuzzy control structure has fewer fuzzy rules, but also the controlled system has a good control performance. Finally, an inverted pendulum control problem is given to illustrate the effectiveness of the proposed control scheme.

Imprecise information is represented by fuzzy disjunctive information, and an extended fuzzy relational model is used to accommodate such information. In the presence of imprecise information, answers to a query can be categorized into two kinds of answers: sure answers and possible answers. To find more likely answers to a given query, the authors develop a method to measure the matching strength of each tuple as an answer to the query. The quality of an answer is higher in the case where less extra information is required and the more sure information is provided.

Article Free Access Share on A sound and complete query evaluation algorithm for relational databases with disjunctive information Authors: L. Y. Yuan Database Research Laboratory, Department of Computing Science, University of Alberta, Edmonton, CANADA T6G 2H1 Database Research Laboratory, Department of Computing Science, University of Alberta, Edmonton, CANADA T6G 2H1View Profile , D.-A. Chiang The Center for Advanced Computer Studies, University of Southwestern Louisiana, Lafayette, LA The Center for Advanced Computer Studies, University of Southwestern Louisiana, Lafayette, LAView Profile Authors Info & Claims PODS '89: Proceedings of the eighth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systemsMarch 1989 Pages 66–74https://doi.org/10.1145/73721.73727Online:29 March 1989Publication History 15citation160DownloadsMetricsTotal Citations15Total Downloads160Last 12 Months7Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF