Until now, research on consumers’ purchasing behavior has primarily focused on psychological aspects or depended on consumer surveys. However, there may be a gap between consumers’ self-reported perceptions and their observable actions. In response, this study aimed to investigate consumer purchasing behavior utilizing a big data approach. To this end, this study investigated the purchasing patterns of fashion items, both online and in retail stores, from a data-driven perspective. We also investigated whether individual consumers switched between online websites and retail establishments for making purchases. Data on 516,474 purchases were obtained from fashion companies. We used association rule analysis and K-means clustering to identify purchase patterns that were influenced by customer loyalty. Furthermore, sequential pattern analysis was applied to investigate the usage patterns of online and offline channels by consumers. The results showed that high-loyalty consumers mainly purchased infrequently bought items in the brand line, as well as high-priced items, and that these purchase patterns were similar both online and in stores. In contrast, the low-loyalty group showed different purchasing behaviors for online versus in-store purchases. In physical environments, the low-loyalty consumers tended to purchase less popular or more expensive items from the brand line, whereas in online environments, their purchases centered around items with relatively high sales volumes. Finally, we found that both high and low loyalty groups exclusively used a single preferred channel, either online or in-store. The findings help companies better understand consumer purchase patterns and build future marketing strategies around items with high brand centrality.

이 논문의 목적은 빅데이터 분석기법의 하나인 연관규칙 분석법을 이용하여 소비자가 구매하는 신선식품 간의 상호 연관성을 살펴보는 것이다. 농촌진흥청의 「농식품 소비자 패널조사」에서 가공식품을 제외한 신선식품의 구매내역 정보를 이용하여 전통시장, 대형마트, 기업형 슈퍼마켓에서 나타나는 연관규칙을 계절별로 분석하였다. 소비자를 2011년을 기준년도로 하여 30, 40, 50대로 구분한 후에, 연령대･구입장소별로 도출된 연관규칙 을 매년 등장한 규칙, 빈번하게 나타난 규칙, 새로 생성된 규칙 세 가지로 구분하였다. 또한 각 연도별로 나타난 공통된 연관규칙에서 향상도의 변화를 분석하여 장바구니에서 나타나는 연관 구매의 동태적인 변화 패턴을 살펴보았다. 분석결과는 소매점포가 묶음상품을 개발하거나 매대를 구성할 때 또는 소비자에게 발송할 상품 홍보용 전단지를 만들 때 유용하게 사용될 것이다.

In this paper, we present a temporal association rule based on item time intervals. A temporal association rule is an association rule that holds specific time intervals. If we consider itemset in the frequently purchased period, we can discover more sign

Association rules are the discovery of previously unknown, potentially useful and hidden knowledge in databases. Many algorithms have been proposed to find association rules in databases. Due to the diverse use's interest and preference to items, former algorithms do not work well in real world application. That is to say, in most algorithms of mining association rules, the items are considered to have equal time weight and are not dealt with quantitative attributes. Hence, to improve former algorithms, we propose an algorithm in this paper to mine fuzzy association rules considering time weight of each item and quantity of each item.

제조 기업들은 공정 내에 불량을 파악하고 품질 특성치를 찾아내기 위해서 대용량의 샘플 데이터를 수집하며 분석하고 있다. 이렇게 수집되어진 데이터를 분석하기 위하여 데이터마이닝 기법이 많이 이용되어지고 있다. 본 연구에서는 제조 공정내의 불량 요인의 데이터를 수집하고 수집된 데이터를 데이터마이닝 기법 중 연관규칙을 이용하여 공정 내 불량간의 연관관계를 파악하고 공정 불량요인을 효과적으로 분석함으로서 제조 공정 내에 불량항목과 공정 간의 변화패턴 관계를 알아보기 위함이다.

In this paper, we address a mining association rules with weighted items and multiple minimum support. We generalize this to the case where items are given weights to reflect their importance to the user. And to find rules that involve both frequent and rare items, we specify multiple minimum supports to reflect the frequency of the items. In rule mining, different rules may need to satisfy different minimum supports depending on what items are in the rules.

In this paper, we present a temporal association rules based on item time intervals. A temporal association rules is an association rule that holds specific time intervals. If we consider itemset in the frequently purchased period, we can discovery more significant itemset satisfying minimum support. Because the previous study did not consider the time interval between purchased item, it could find itemset that did not satisfy the minimum support in case some item was frequently purchased in a specific period and rarely or not purchased in other period. Our approach use interval support which is counted by period with support and confidence in the association rule to discovery large itemset.

We study data mining technique in an electronic commerce. Customers travel web pages in an shopping mall and they sometimes purchase products. It is important for a web master in a shopping mall to know customer's purchasing patterns. We discover both association rules among customer's purchasing products and customer's traversal paths. We propose three phase mining technique to explore it. In the first phase, it find large items from sales database. In the second phase, it add to traversal paths. In the third phase, it discover associations rules from large items.