A Machine Learning Algorithm for Analyzing String Patterns Helps to Discover Simple and Interpretable Business Rules from Purchase History

Hamuro, Yukinobu; Kawata, Hideki; Katoh, Naoki; Yada, Katsutoshi

doi:10.1007/3-540-45884-0_43

Cited by 11 publications

(10 citation statements)

References 8 publications

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“…E-BONSAI system was recently developed by improving the BONSAI system that has been developed for genome analysis so as to adapt it to the above purpose [4]. E-BONSAI system has several advantages over other systems.…”

Section: The Purpose and Contribution In This Papermentioning

confidence: 99%

“…In recent years, many retail stores have started to introduce frequent shoppers program (FSP) by which sales data with customer ID are becoming available for use. However, the existing research has not made use of such detailed sequential data [4][17]. If we can build a forecasting model which takes into account purchase behavior of individual customers in the past, it will not only become possible to construct a forecasting model with higher precision but to discover knowledge that helps creation of effective business action.…”

Section: Previous Workmentioning

confidence: 99%

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Business Application for Sales Transaction Data by Using Genome Analysis Technology

2003

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Abstract. We have recently developed an E-BONSAI (Extended BON-SAI) for discovering useful knowledge from time-series purchase transaction data, developed by improving and adding new features to a machine learning algorithm for analyzing string pattern such amino acid sequence, BONSAI, proposed by Shimozono et al. in 1994. E-BONSAI we developed can create a good decision tree to classify positive and negative data for records whose attributes are either numerical, categorical or string patterns while other methods such as C5.0 and CART cannot deal with string patterns directly. We shall demonstrate advantages of E-BONSAI over existing methods for forecasting future demands by applying the methods to real business data. To demonstrate an advantage of E-BONSAI for business application, it is significant to evaluate it from the two perspectives. The first is the objective and technical perspective such as the prediction accuracy. The second is the management perspective such as the interpreterability to create new business action. Applying the E-BONSAI to forecast how long new products survive in instant noodle market in Japan, we have succeeded in attaining high prediction ability and discovering useful knowledge for domain experts.

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Section: The Purpose and Contribution In This Papermentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

Business Application for Sales Transaction Data by Using Genome Analysis Technology

2003

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“…At the time that we devised this system, we made use of a previously existing system known as EBONSAI. EBONSAI (Hamuro et al 2002b;Yada et al 2007) is a time series analysis technique adapted from the BONSAI character parsing approach employed for the genome project. Up to now, EBONSAI had been used for time series analyses of sales data, web log data, and the like, but it had never been used for the kind of stream data that is generated by RFID.…”

Section: Purpose Of This Researchmentioning

confidence: 99%

“…We introduce knowledge expressions in the form of character strings for stream data including information about customer movements, and have proposed the adoption of EBONSAI (Hamuro et al 2002b;Yada et al 2007), a character parsing application used in the field of business. In other words, by abstracting information on the paths that customers trace within a store and expressing that information in the form of character strings, we thought to implement rule-based abstraction using existing character string parsing algorithms.…”

Section: Introductionmentioning

confidence: 99%

String analysis technique for shopping path in a supermarket

Yada

2009

J Intell Inf Syst

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The sensor network technology developed in recent years has made it possible to accurately track the in-store behavior of customers which was previously indeterminable. The information on the in-store behavior of customers obtained by using this technology, namely information on their shopping path, provides us with useful information concerning the customer's purchasing behavior. The purpose of this research is to apply character string analysis techniques to shopping path data so as to analyze customers' in-store behavior, and thereby clarify technical problems with them (the character string analysis techniques) as well as their usability. In this paper we generated character strings on visit patterns to store sections by focusing exclusively on customers stopping by these sections in order to elucidate the visiting patterns of customers who made a large quantity of purchases. In this paper, we were able to discover useful information by using the character string analysis technique EBONSAI, thereby illustrating the usability and usefulness of character string analysis techniques in shopping path analysis.

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“…We introduce knowledge expression character strings for stream data includ about customer movements, and hav adoption of EBONSAI [7] [15], a ch application used in the field of business by abstracting information on the path trace within a store and expressing tha the form of character strings, we thoug rule-based abstraction using existing parsing algorithms. The application technology to a new field not only d usefulness of that technology but als technological issues at the same time.…”

Section: Introductionmentioning

confidence: 99%