Study of WF-C-Means Algorithm for CBR Systems

A successful Case-Based Reasoning (CBR) system lightly depends on how to design an accurate and efficient case retrieval mechanism. Brand extension is widely applied as a corporate strategy in the economic life. Some companies succeed in brand extension, some fail. So it is very important to study the case and theory of brand extension. Brand extension has its own background and reasons. Brand extension is not only the way to use the brand equity but also the way to increase the brand equity. Brand extension has different types including line extension, associated brand, etc. Different type of brand has different brand extension capacity. The brand that belongs to nonobjective type has best extension capacity. The extension capacity of brand that belongs to functional or technical type cannot compare with nonobjective type. In this research we propose a Weighted Feature C-means clustering algorithm (WF-C-means) to group all prior cases in the case base into several clusters. In the last few years the catena supermarket had the quick development in the our country , but produce the power not clear in meaning , conduct not norm ,the merchandise man agent level fall behind , the marketing management level is low , conducting the cost to reside further develop met that the high not inferior problem obstructed the catena supermarket . For this, the catena supermarket busyness enterprise should ask for help the policy to stupor, managing as soon as possible to produce the power relation fluently, make use of the information technique exaltation cost control ability, and build up the new relation with supplier. Through our experiments, the efforts of this research are useful for real world CBR systems.