Weighted Association Rules Research Articles

WAR: Weighted Association Rules for Item Intensities

In this paper, we extend the traditional association rule problem by allowing a weight to be associated with each item in a transaction to reflect the interest/intensity of each item within the transaction. In turn, this provides us with an opportunity to associate a weight parameter with each item in a resulting association rule; we call them weighted association rules (WAR). One example of such a rule might be 80% of people buying more than three bottles of soda will also be likely to buy more than four packages of snack food, while a conventional association rule might just be 60% of people buying soda will be also be likely to buy snack food. Thus WARs cannot only improve the confidence of the rules, but also provide a mechanism to do more effective target marketing by identifying or segmenting customers based on their potential degree of loyalty or volume of purchases. Our approach mines WARs by first ignoring the weight and finding the frequent itemsets (via a traditional frequent itemset discovery algorithm), followed by introducing the weight during the rule generation. Specifically, the rule generation is achieved by partitioning the weight domain space of each frequent itemset into fine grids, and then identifying the popular regions within the domain space to derive WARs. This approach does not only support the batch mode mining, i.e., finding WARs for the dataset, but also supports the interactive mode, i.e., finding and refining WARs for a given (set) of frequent itemset(s).

WAR: Weighted association rules for item intensities

WAR: Weighted association rules for item intensities

Mining weighted association rules

Association rules are useful for determining correlations between items and have applications in marketing, financial and retail sectors. Lots of algorithms have been proposed for finding the association rules in databases. Most of these algorithms treat each item as uniformity. However, in real applications, the user may have more interest in the rules that contain those fashionable items that occur frequently. Usually too many outdated items exist in databases, but they seldom occur recently. Those outdated items hamper us to find the interesting rules efficiently and effectively. Another case is the user sometimes may want to mine the association rules with more emphasis on some items. To solve these problems, in this paper, we propose the vertical and mixed weighted association rules. We can divide the database into several time intervals, and assign a weight for each interval. Furthermore, we also assign a weight for each item to identify the important items. We present an algorithm MWAR (Mixed Weighted Association Rules) to handle the problem of mining mixed weighted association rules. The experiments show that the rules from our methods have much better predictive ability on future data. We also demonstrate the efficiency of our methods on real data and synthetic datasets.

Expert Fuzzy-Neuro Controller Design for Wall Climbing Robot for Decontamination of Nuclear-Power Station

The arrangement principles and design methodology for complex control framework of Al control systems are introduced. The notions of intelligence levels with top boundary (""intelligence in large"") and the bottom boundary (""intelligence in small"") are defined. Special methodology of Al control system design for decontamination of nuclear-power station (IMPS) on the base of a wall-climbing robot (WCR) with various intelligence levels is considered. The basis of this methodology is computer simulation of dynamics for mechanical systems with the help of qualitative physics and search for possible solutions by genetic algorithm (GA). On artificial neural networks, optimal solutions are obtained and a knowledge base of fuzzy controller on WARP (Weight Associative Rule Processor) is formed. Strategy for planning, environment recognition using two types of sensors, and locomotion control to realize autonomous locomotion of the mobile robot are described. The WCR and the mobile robot for horizontal displacement with manipulators are moved in unstructured environments. Fuzzy qualitative simulation, GA and hierarchical node map, and fuzzy neural network (FNN) have demonstrated their effectiveness for path planning of the mobile robots. The results of fuzzy robot control simulation, monitoring, and experimental investigations are presented. The application of WARP to design automatic fuzzy controller for fuzzy correction motion of manipulator and WCR is examined.

WARP: Weight associative rule processor

WARP: Weight associative rule processor