A Data Mining System for Managing Customer Relationship

This paper presents a data mining study that aims to identify potential high-value visitors for a drugstore chain in Japan. Our purpose is to provide timely decision support to the marketing and service departments for managing customer relationship. The conceptualization of customer value is discussed and is differentiated from a more commonly used construct, customer loyalty. We briefly describe the data mining system that supports the study. Our result show two supervised learning methods are comparable in terms of predictive accuracy

Discovering association strength among brand loyalties from purchase history

Analyzing purchase history of customers enables us to discover valuable knowledge that is helpful for developing effective sales promotion. In this respect, we shall introduce a new notion, association strength among brand loyalties, which is defined for every ordered pair of brands. If the association strength between loyalties of brands A and B is high, it represents that purchase of brand A is highly correlated to that of brand B. Conventional method for discovering associative purchasing is usually applied for one purchase opportunity (one receipt), i.e., it reveals how often two commodities are purchased at the same time. On the other hand, we are interested in discovering relationship among customers’ loyalties to certain brands or manufacturers by investigating long-term purchase history of customers. By computing association strengths from customers’ purchase history of drugstore chain in Japan, we could produce several interesting rules that will be useful for sales promotion planningISIE 2001, June 12-16, 2001, Paradise Hotel Pusan, Kore

The Future Direction of New Computing Environment for Exabyte Data in the Business World

With the rapid spread of the Internet and the computerization of trading a huge amount of data on the Internet and of transaction database in enterprises has been accumulated. The purpose of this paper is to explain the significance of the technology to process of exabyte-scale data and presents the business application, CODIRO, which will make it possible to integrate various types of large scale data. CODIRO is a consumer research system which discovers new knowledge by integrating the huge amount of different types of data both on the Internet and within companies. This paper will demonstrate the business implications for exabyte-scale information technology research, by explaining an example of the analysis of the sales effectiveness of television commercials using CODIRO.2005 IEEE/IPSJ International Symposium on Applications and the Internet Workshops (SAINT 2005 Workshops), 31 January - 4 February 2005, Trento, Italy

New modified laparoscopic Davydov’s method using the rudimentary uterus

Among surgical procedures of Mayer-Rokitansky-Kuster-Hauser (MRKH) syndrome, the laparoscopic Davydov's technique seems to offer the most feasible and effective approach for creating a neovagina. Several reports have pointed at the necessity for mobilization of the peritoneum to obtain a longer neovagina and have reported a modified laparoscopic Davydov’s method. A new method was performed for a 24- and an 18-year-old patient. The most significant method in present procedure was to leave the thickened tissue that connects both rudimentary uteruses. The advantages of present procedure are physiological, creating a longer neovagina. Furthermore, this approach may help prevent prolapse of pelvic organs by leaving the thickened tissue as a ceiling

Laparoscopic resection of two peritoneal loose bodies on the rectosigmoid colon

Laparoscopic examination of a 77-year-old woman revealed two peritoneal loose bodies connected to fatty appendices on the rectosigmoid colon and resected at the stalks. The peritoneal loose bodies were found to be fat-containing masses on preoperative magnetic resonance imaging, and postoperative pathological examination revealed fat degeneration tissue with or without fibrous outer layers

Essential roles of DC-derived IL-15 as a mediator of inflammatory responses in vivo

Interleukin (IL)-15 is expressed in a variety of inflammatory diseases. However, the contribution of dendritic cell (DC)–derived IL-15 to the development of diseases is uncertain. Using established models of Propionibacterium acnes (P. acnes)– and zymosan-induced liver inflammation, we observed granuloma formation in the livers of wild-type (WT) and RAG-2−/− mice but not in those of IL-15−/− mice. We demonstrate that this is likely caused by an impaired sequential induction of IL-12, IFN-γ, and chemokines necessary for monocyte migration. Likewise, lethal endotoxin shock was not induced in P. acnes– and zymosan-primed IL-15−/− mice or in WT mice treated with a new IL-15–neutralizing antibody. In both systems, proinflammatory cytokine production was impaired. Surprisingly, neither granuloma formation, lethal endotoxin shock, nor IL-15 production was induced in mice deficient for DCs, and adoptive transfer of WT but not IL-15−/− DCs restored the disease development in IL-15−/− mice. Collectively, these data indicate the importance of DC-derived IL-15 as a mediator of inflammatory responses in vivo

mbonsai: Application Package for Sequence Classification by Tree Methodology

In many applications such as transaction data analysis, the classification of long chains of sequences is required. For example, brand purchase history in customer transaction data is in a form like AABCABAA, where A, B, and C are brands of a consumer product. The decision tree-based package mbonsai is designed to handle sequence data of varying lengths using one or multiple variables of interest as predictor variables. This software package uses tree growing and pruning strategies adopted from C4.5 and CART algorithms, and includes new features for handling sequence data and indexing for classification purpose. The software uses a simple command line program for learning and predicting processes, and has the ability to generate user-friendly graphics depicting decision trees. The underlying C++ codes are designed to efficiently process large data sets in ASCII files. Two examples from transaction data sets are used to illustrate the application of mbonsai