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Fuzzy classification is one of the most important applications of fuzzy logic. Its goal is to find a set of fuzzy rules which describe classification problems. Most of the existing fuzzy rule induction methods (e.g., the fuzzy decision trees induction method) focus on searching rules consisting of t-norms (i.e., AND) only, but not t-conorms (OR) explicitly. This may lead to the omission of generating important rules which involve t-conorms explicitly. This paper proposes a type of tree termed pattern trees which make use of different aggregations including both t-norms and t-conorms. Like decision trees, pattern trees are an effective machine learning tool for classification applications. This paper discusses the difference between decision trees and pattern trees, and also shows that the subsethood based method (SBM) and the weighted subsethood based method (WSBM) are two specific cases of pattern trees, with each having a fixed pattern tree structure. A novel pattern tree induction method is proposed. The comparison to other classification methods including SBM, WSBM and fuzzy decision tree induction over datasets obtained from UCI dataset repository shows that pattern trees can obtain higher accuracy rates in classifications. In addition, pattern trees are capable of generating classifiers with good generality, while decision trees can easily fall into the trap of over-fitting. According to two different configurations, simple pattern trees and pattern trees have been distinguished. The former not only produce high prediction accuracy, but also preserve compact tree structures, while the latter can produce even better accuracy, but as a compromise produce more complex tree structures. Subject to the particular demands (comprehensibility or performance), simple pattern trees and pattern trees provide an effective methodology for real world applications. Weighted pattern trees have been proposed in which certain weights are assigned to different trees, to reflect the nature that different trees may have different confidences. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that both weighted and unweighted pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined. [ABSTRACT FROM AUTHOR]

A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules connected with t-conorms explicitly. Pattern trees can not only obtain high accuracy rates in classification applications, but also be robust to over-fitting. This paper further extends pattern trees approach by assigning certain weights to different trees, to reflect the nature that different trees may have different confidences. The concept of weighted pattern trees is important as it offers an option to trade off the complexity and performance of trees. In addition, it enhances the semantic meaning of pattern trees. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both of them are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that (weighted) pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined. [ABSTRACT FROM AUTHOR]

In telecom industry high installation and marketing costs make it between six to ten times more expensive to acquire a new customer than it is to retain the existing one. Prediction and prevention of customer churn is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is lots of related temporal data sequences generated as a result of customer interaction with the service provider. Existing churn prediction methods like decision tree typically just classify customers into churners or non-churners while completely ignoring the timing of churn event. Given histories of other customers and the current customer's data, the presented model proposes a new k nearest sequence (kNS) algorithm along with temporal sequence fusion technique to predict the whole remaining customer data sequence path up to the churn event. It is experimentally demonstrated that the new model better exploits time-ordered customer data sequences and surpasses the existing churn prediction methods in terms of performance and offered capabilities. [ABSTRACT FROM AUTHOR]

Modern business face the challenge of to make use of one of there most valuable assets - data about their customers and processes - in real time in order to stay ahead of global competition. In order to achieve real time business intelligence it is necessary to automate data analysis to address the lack of available experts, empower business users and produce analysis results where and when they are required. In this paper we describe how our intelligent data analysis platform SPIDA automatically selects suitable data analysis methods for application to data analysis problems. [ABSTRACT FROM AUTHOR]