Regular papers

 

Applying Dynamic Causal Mining in Retailing

 

Yi Wang

 

Nottingham Business School, Park Row, floor 2, Nottingham Trent University Burton Street, Nottingham, NG1 4BU, U.K.

 

Manuscript received May 10, 2008.
Manuscript accepted for publication June 20, 2008.

 

Abstract

With the fast development of information technology, retailers are suffering from the excess of information. Too much information can be a problem. However, more information creates more opportunity. In retailing, information is the key issue to maximizing revenue. It is now hard to make timely or effective decisions and to the right content to the right place, at the right time and in the right form. This paper is about managing the information so that the user can gain more clear insight. It is about integrating and inventing methods and techniques. The Semantic Web will provide a foundation for such a solution. However, semantics only provide a way of mapping the content of a web to user defined annotations. Not many companies have fully utilized the power of Internet retailing due to the various technical obstacles have yet to be overcome. The existing research in e–retailing focuses only on the traditional retailing including direct and indirect retailing approaches. This paper suggests that applying association mining techniques can further improve the dealing of information overload in a web oriented retailing environment.

Key words: Semantic web, online retailing, data mining, formal concept, Protégé, triple store, Sparql.

 

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The databases were contributed by many researchers, mostly from the field of machine learning and data mining and collected by the machine learning group at the University of California, Irvine. Two of the datasets, MCslom and ASW, are taken from real life. These data sets are described briefly below

Cystine Database: This data arises from a large study to examine EEG correlates of genetic predisposition to alcoholism. It contains measurements from 64 electrodes placed on the scalp sampled at 256 Hz (3.9–msec epoch) for 1 second.

Weka base: This dataset contains time series sensor readings of the Pioneer–1 mobile robot. The data is time series, multivariate. The few are binary coded 0.0 and 1.0. Two categorical variables are included to delineate the trials within the datasets. The data is broken into "experiences", in which the robot takes action for some period of time and experiences a controlled interaction with its environment.

Market basket: Classical association data mining, used in WeKa analysis. It consists of 100 different transactions.

Bank data: Includes 600 instances of bank transactions and 12 attributes in each instance.

ASW: This data consists of real life data. This dataset contains 65536 attributes of metal manufacturing, with 8 records in each attribute.

Mclosom: Manufacturing database for logistics. 72 time stamps and 50 attributes for 5 different classes.