In recent years industries have increasingly used customer relationship labor in management as a tool to improve their position in the marketplace. This research involves using a data warehouse, decision-tree-based data mining, and neural network pattern classification analysis to isolate the causes of non-conformity in IC packaging. The correctness of the classification analysis produced using the two methods is compared. Our objective is to establish an information analysis system, that is able to quickly identify the causes of problems thereby reducing the time taken to solve quality-related problems. It is shown that predictions made about the target group using decision tree analysis are more accurate than those made by neural network classification, indicating that decision tree analysis is an effective means of classification analysis of a company's quality problems.

2 Literature Review y all data required data processing. These ata conversion into the data bstract:-The aims of this paper is to depict an intelligent quality analysis control system involved in using dat arehouse, data mining, decision tree, and Bayesian classification analysis to discover the main inconsistency asons in the manufacturing process of semiconductor packaging plants and compare the correctness of assification analysis of the two methods, so as to set up an intelligent quality analysis control system providing efficiency tool for analyzing problems, with a view to identifying the causes of problems, making decision mediately, and eventually reducing the cycle time taken to solve quality-related problems. he contributions of this research are illustrated as follows. Predictions made by the target group by means o ecision tree analysis are more accurate than those made by Bayesian classification, indicating that decision tree alysis is an effective mean of classification analysis in semiconductor packaging quality problems, whereas aluation of feasible methods by data warehouse, and data mining followed by establishment of the basis for a uality analysis system environment, that is characteristic of knowledge sharing may be applied to analysis of the uality problems in all corporation.

First, we classify the selected customers into clusters using RFM model to identify high-profit, gold customers. Subsequently, we carry out data mining using association rules algorithm. We measure the similarity, difference and modified difference of mined association rules based on three rules, i.e. Emerging Patten Rule, Unexpected Change Rule, and Added/Perished Rule. In the meantime, we use rule matching threshold to derive all types of rules and explore the rules with significant change based on the degree of change measured. In this paper, we employ data mining tools and effectively discover the current spending pattern of customers and trends of behavioral change, which will allow management to detect in a large database potential changes of customer preference, and provide as early as possible products and services desired by the customers to expand the clientele base and prevent customer attrition.

SUMMARYThe exchanged hypercube, denoted by EH(s, t), is a graph obtained by systematically removing edges from the corresponding hypercube, while preserving many of the hypercube's attractive properties. Moreover, ring-connected topology is one of the most promising topologies in Wavelength Division Multiplexing (WDM) optical networks. Let R n denote a ring-connected topology. In this paper, we address the routing and wavelength assignment problem for implementing the EH(s, t) communication pattern on R n , where n = s + t + 1. We design an embedding scheme. Based on the embedding scheme, a near-optimal wavelength assignment algorithm using 2 s+t−2 + 2 t /3 wavelengths is proposed. We also show that the wavelength assignment algorithm uses no more than an additional 25 percent of (or 2 t−1 /3 ) wavelengths, compared to the optimal wavelength assignment algorithm.