Mining customer knowledge for electronic catalog marketing

Direct marketing is the use of the telephone and non-personal media to communicate product and organizational information to customers, who then can purchase products via mail, telephone, or the Internet. In contrast, catalog marketing is a type of marketing in which an organization provides a catalog from which customers make selections and place orders by mail or telephone. However, most catalogs for retailing firms are presented to customers in the format of paper catalogs without strategic segmentation design and implementation. In this regard, electronic catalog design and marketing could be a method to integrate the Internet and catalog marketing using market segmentation in order to enhance the effectiveness of direct marketing and sales management in retailing. This paper uses data mining based on association rules from relational database design and implementation for mining customer knowledge. As result, marketing knowledge patterns and rules are extracted for the electronic catalog marketing and sales management of a retailing mall in Taiwan.     

Mining extremely small data sets with application to software reuse

A serious problem encountered by machine learning and data mining techniques in software engineering is the lack of sufficient data. For example, there are only 24 examples in the current largest data set on software reuse. In this paper, a recently proposed machine learning algorithm is modified for mining extremely small data sets. This algorithm works in a twice‐learning style. In detail, a random forest is trained from the original data set at first. Then, virtual examples are generated from the random forest and used to train a single decision tree. In contrast to the numerous discrepancies between the empirical data and expert opinions reported by previous research, our mining practice shows that the empirical data are actually consistent with expert opinions. Copyright © 2008 John Wiley & Sons, Ltd.     

Information methods for model selection in linear mixed effects models with application to HCV data

In this paper, we derive a small sample Akaike information criterion, based on the maximized loglikelihood, and a small sample information criterion based on the maximized restricted loglikelihood in the linear mixed effects model when the covariance matrix of the random effects is known. Small sample corrected information criteria are proposed for a special case of linear mixed effects models, the balanced random-coefficient model, without assuming the random coefficients covariance matrix to be known. A simulation study comparing the derived criteria and several others for model selection in the linear mixed effects models is presented. We illustrate the behavior of the studied information criteria on real data from a study of subjects coinfected with HIV and Hepatitis C virus. Robustness of the criteria, in terms of the error distributed as a mixture of normal distributions, is also studied. Special attention is given to the behavior of the conditional AIC by Vaida and Blanchard (2005). Among the studied criteria, GIC performs best, while cAIC exhibits poor performance. Because of its inferior performance, as demonstrated in this work, we do not recommend its use for model selection in linear mixed effects models.     

Mining marketing maps for business alliances

A business can strengthen its competitive advantage and increase its market share by forming a strategic alliance. With the help of alliances, businesses can bring to bear significant resources beyond the capabilities of the individual co-operating firms. Thus how to effectively evaluate and select alliance partners is an important task for businesses because a successful corporation partner selection can therefore reduce the possible risk and avoid failure results on business alliance. This paper proposes the Apriori algorithm as a methodology of association rules for data mining, which is implemented for mining marketing map knowledge from customers. Knowledge extraction from marketing maps is illustrated as knowledge patterns and rules in order to propose suggestions for business alliances and possible co-operation solutions. Finally, this study suggests that integration of different research factors, variables, theories, and methods for investigating this research topic of business alliance could improve research results and scope.     

The Catalog Management Strategy of Distributed Data Base Systems

In this paper the catalog management strategy of the successfully integrating and running DDBMS C-POREL is summarized.The new catalog management strategy and its implementation scheme are based on the analysis of the catalog management methods of the pioneer DDBMS.The goal of the new strategy is to improve the system efficiency.Analysis and practice show that this strategy is successful.     

New separating hyperplane method with application to the optimisation of direct marketing campaigns

In this article we present a new class of separating hyperplane methods for the binary classification task. Our hyperplanes have a very low Vapnik-Chervonenkis dimension, so they generalise well. Geometrically, our approach is based on searching of a proper pair of observations from different classes of the explained variable. Once this pair is found the discriminant hyperplane becomes orthogonal to the line connecting these observations. This method allows the direct optimisation of any prediction criterion, not necessary the fraction of correctly classified observations. Models generated by this technique have low computational complexity and allow fast classification. We illustrate the performance of our method by applying it to the problem of optimisation of direct marketing campaigns, where the natural measure of the prediction performance is the lift curve.     

区域地理空间数据共享平台与目录服务研究

分布式、异构的地理空间数据的整合与共享是空间信息网络服务的关键技术。构建了基于SOA和XML Web Services技术的地理空间数据共享平台和目录服务体系,提出了建设区域地理空间数据共享服务平台的技术流程。研究的共享体系和目录服务体系能够有效地解决地理空间数据共享中信息组织、检索、定位、透明访问和集成应用的关键问题。     

Robust regression with application to symbolic interval data

This paper presents a robust regression model that deals with cases that have interval-valued outliers in the input data set. Each interval of the input data is represented by its range and midpoint and the fitting to interval-valued data is not sensible in the presence of midpoint and/or range outliers on the interval response. The predictions of the lower and upper bounds of new intervals are performed and simulation studies are carried out to validate these predictions. Two applications with real-life interval data sets are considered. The prediction quality is assessed by a mean magnitude of relative error calculated from a test data set.     

Mining changes in customer behavior in retail marketing

During the past decade, there have been a variety of significant developments in data mining techniques. Some of these developments are implemented in customized service to develop customer relationship. Customized service is actually crucial in retail markets. Marketing managers can develop long-term and pleasant relationships with customers if they can detect and predict changes in customer behavior. In the dynamic retail market, understanding changes in customer behavior can help managers to establish effective promotion campaigns. This study integrates customer behavioral variables, demographic variables, and transaction database to establish a method of mining changes in customer behavior. For mining change patterns, two extended measures of similarity and unexpectedness are designed to analyze the degree of resemblance between patterns at different time periods. The proposed approach for mining changes in customer behavior can assist managers in developing better marketing strategies.     

Permutation test for incomplete paired data with application to cDNA microarray data

A paired data set is common in microarray experiments, where the data are often incompletely observed for some pairs due to various technical reasons. In microarray paired data sets, it is of main interest to detect differentially expressed genes, which are usually identified by testing the equality of means of expressions within a pair. While much attention has been paid to testing mean equality with incomplete paired data in previous literature, the existing methods commonly assume the normality of data or rely on the large sample theory. In this paper, we propose a new test based on permutations, which is free from the normality assumption and large sample theory. We consider permutation statistics with linear mixtures of paired and unpaired samples as test statistics, and propose a procedure to find the optimal mixture that minimizes the conditional variances of the test statistics, given the observations. Simulations are conducted for numerical power comparisons between the proposed permutation tests and other existing methods. We apply the proposed method to find differentially expressed genes for a colorectal cancer study.     

A generalized gamma distribution with application to drought data

Gamma distributions are some of the most popular models for hydrological processes. In this paper, a very flexible family which contains the gamma distribution as a particular case is introduced. Evidence of flexibility is shown by examining the shape of its probability density function (pdf). A treatment of the mathematical properties is provided by deriving expressions for the n th moment. Estimation and simulation issues are also considered. Finally, a detailed application to drought data from the State of Nebraska is illustrated.     

Unsupervised learning with mixed numeric and nominal data

Presents a similarity-based agglomerative clustering (SBAC) algorithm that works well for data with mixed numeric and nominal features. A similarity measure proposed by D.W. Goodall (1966) for biological taxonomy, that gives greater weight to uncommon feature value matches in similarity computations and makes no assumptions about the underlying distributions of the feature values, is adopted to define the similarity measure between pairs of objects. An agglomerative algorithm is employed to construct a dendrogram, and a simple distinctness heuristic is used to extract a partition of the data. The performance of the SBAC algorithm has been studied on real and artificially-generated data sets. The results demonstrate the effectiveness of this algorithm in unsupervised discovery tasks. Comparisons with other clustering schemes illustrate the superior performance of this approach     

用Grover量子搜索算法挖掘网络数据

利用Grover量子搜索算法和概率论给出了挖掘网络数据的关联规则挖掘、权威页面挖掘和Weblog记录挖掘的一种新方法,最后说明该方法比任何经典方法要快得多。     

A Shortest-route approach for optimum aggregation of data with an application to income data

This paper addresses the problem of grouping a large volume of discrete data into a given number of groups. The algorithm is based on the well-known shortest path algorithm. It handles different criteria for minimizing the loss of information which might result during the process of grouping. The given problem is transformed into a directed graph and it then looks for a shortest path with a given number of edges. The algorithm is illustrated through an example of five data items. It has also been coded in Pascal and some test results are presented.     

Computation of transfer function data from frequency response data with application to data-based root-locus

This paper describes the computation and use of transfer function data (TFD) computed from frequency response data of a system. TFD can be regarded as a sampled, data-based representation of the transfer function. TFD can be computed from frequency response data for stable, lightly damped systems using a Cauchy integral. Computational accuracy and complexity are extensively discussed. As a use-case of TFD it is shown that a root-locus can be computed in a data-based way, using only frequency response data of a system. Experiments on a benchmark motion system demonstrate the use of TFD in minimizing the settling time.     

Interactive mosaic building and its application to marketing strategies using NFC

Multimedia Tools and Applications - Although the building of photo mosaics has been widely studied, the existing solutions are not appropriate for solving problems where the photos are not already...