• Journal of the Korean Data and Information Science Society
• /
• v.19 no.2
• /
• pp.551-558
• /
• 2008

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. If there is many item in the association rule, much time is required. Xiong(2004) studies new method which is to compute the support of upper. They used support of upper to the $^{\theta}$. But $^{\theta}$ is subjective. In this paper, we present statistical objective criterion for efficiently identifying correlated pairs.

• Proceedings of the Korean Information Science Society Conference
• /
• 2005.11a
• /
• pp.973-975
• /
• 2005

With the continuous growth of e-commerce there is a huge amount of products information available online. Shop managers expect to apply information techniques to increase profit and perfect service. Hence many e-commerce systems use association rule mining to further refine their management. However previous association rule algorithms have two limitations. Firstly, they only use the number to weight item's essentiality and ignore essentiality of item profit. Secondly, they did not consider the relationship between number and profit of item when they do mining. We address a novel algorithm, profit-based association rule algorithm that uses profit-based technique to generate 1-itemsets and the multiple minimum supports mining technique to generate N-items large itemsets.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.3
• /
• pp.903-911
• /
• 2008

Association rule mining searches for interesting relationships among items in a given large database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. Many data is most quantitative data. There is a need for partitioning techniques to quantitative data. The partitioning process is referred to as binning. We introduce several binning methods ; parameter mean binning, equi-width binning, equi-depth binning, clustering-based binning. So we apply these binning methods to several distribution types of quantitative data and present the best binning method for association rule discovery.

• 한국데이터정보과학회:학술대회논문집
• /
• 2006.11a
• /
• pp.197-203
• /
• 2006

Association rule mining searches for interesting relationships among Items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule support and confidence and lift. If there is many item in the association rule, much time is required. Xiong(2004) studies new method which is to compute the support of upper. They used support of upper to the $\theta$. But $\theta$ is subjective. In this paper, we present statistical objective criterion for efficiently identifying correlated pairs.

• Journal of the Korean Data and Information Science Society
• /
• v.19 no.4
• /
• pp.1141-1151
• /
• 2008

Data mining is the process of sorting through large amounts of data and picking out useful information. One of the well-studied problems in data mining is the exploration of association rules. An association rule technique finds the relation among each items in massive volume database. Some interestingness measures have been developed in association rule mining. Interestingness measures are useful in that it shows the causes for pruning uninteresting rules statistically or logically. This paper propose a conditional pure confidence to evaluate association rules and then describe some properties for a proposed measure. The comparative studies with confidence and pure confidence are shown by numerical example. The results show that the conditional pure confidence is better than confidence or pure confidence.

• Proceedings of the Korean Nuclear Society Conference
• /
• 2002.05a
• /
• pp.180.2-180
• /
• 2002

• Proceedings of the Korean Society of Marine Engineers Conference
• /
• 2005.06a
• /
• pp.246-250
• /
• 2005

Recommand Systems are being used by an ever-increasing number of E-Commerce to help customers find products to purchase. Recommend Systems offer a technology that allows personalized recommendations of items of potential interest to users based on information about similarities and dissimilarities among different customers tastes. In this paper, we design and build a Recommend System using the historical customer movie purchase transactions and extracts the knowledge needed to make association recommendations to new customers.

• School Mathematics
• /
• v.1 no.1
• /
• pp.187-216
• /
• 1999

The purpose of this study is to develop mathematics performance assessment items for the 8th graders and to analyze their performance ability. First, five-themes were selected : 'Calculator', 'Cut and Paste', 'Rule finding', 'Place Assignment', 'My thinking'. Then, the assistance of Mathematics education specialists and Teachers, 10 P. A. items consisting of two subtasks and their evaluation rubric were developed. Then, items were revised by the results of pilot test. And, final version of items were administrated to the 8th graders of three regions(Seoul, Chongiu, Chungp$\acute{y}$ong). Through analyzing the performance ability of the subjects assessment items, the following conclusion were obtained: They were very insufficient in the ability to find some patterns in the given problem situation and to describe logically the patterns in terms of mathematical terminology. It is believed because they were familiar with the objective test to take one or short answer.

• The Research Journal of the Costume Culture
• /
• v.7 no.2
• /
• pp.230-241
• /
• 1999

The purpose of this study is to classify frequency of advertisement and types of advertisement by items and to analyze a primary factors were factors were reflected in the costumes by a policy of rule under the Japanese rule. Data was MaIl Shin Bo\`s advertisement connected with costumes from 1910 to 1945 years. The results are as follows : 1. The order of the advertisement\`s frequency was footwear, cosmetics, soap[, headgear, western style clothes, precious metals and so on. 2. The type of the advertisement was a format that transmits informs in all items. Cosmetics, hairdye and shampoo applied positive appeals. 3. In the military government, the advertisement\`s frequency connected with costumes was 37.5% and a shoe store was the first order. A shoes and headgear were high level, because of these were essential imports and were allowded as a proper articles for a western style clothes by a civilized policy. In the political periods of civilization, the advertisement\`s frequency connected with costumes was 54%. This result indicates industrial development of this period. Soap was the first order during 1924∼1933 and cosmetics was the first order during 1934∼1940. High level of the advertisement\`s frequency in these imports were reflected by a cultural policy as a link of an appeasement measure In the political periods of a racial liquidation, the advertisement\`s frequency connected with costumes was 8.5% and the advertisement\`s order by items was cosmetics, a shoe store. The reason was that reflected the phases of the times that was serious by a shortage of goods and an reinforcement of wartime\`s attitudes throughout war.

• CRM연구
• /
• v.4 no.2
• /
• pp.19-29
• /
• 2011

There is a large difference between purchasing patterns in an online shopping mall and in an offline market. This difference may be caused mainly by the difference in accessibility of online and offline markets. It means that an interval between the initial purchasing decision and its realization appears to be relatively short in an online shopping mall, because a customer can make an order immediately. Because of the short interval between a purchasing decision and its realization, an online shopping mall transaction usually contains fewer items than that of an offline market. In an offline market, customers usually keep some items in mind and buy them all at once a few days after deciding to buy them, instead of buying each item individually and immediately. On the contrary, more than 70% of online shopping mall transactions contain only one item. This statistic implies that traditional data mining techniques cannot be directly applied to online market analysis, because hardly any association rules can survive with an acceptable level of Support because of too many Null Transactions. Most market basket analyses on online shopping mall transactions, therefore, have been performed by expanding the co-occurrence criteria of traditional association rule mining. While the traditional co-occurrence criteria defines items purchased in one transaction as concurrently purchased items, the expanded co-occurrence criteria regards items purchased by a customer during some predefined period (e.g., a day) as concurrently purchased items. In studies using expanded co-occurrence criteria, however, the criteria has been defined arbitrarily by researchers without any theoretical grounds or agreement. The lack of clear grounds of adopting a certain co-occurrence criteria degrades the reliability of the analytical results. Moreover, it is hard to derive new meaningful findings by combining the outcomes of previous individual studies. In this paper, we attempt to compare expanded co-occurrence criteria and propose a guideline for selecting an appropriate one. First of all, we compare the accuracy of association rules discovered according to various co-occurrence criteria. By doing this experiment we expect that we can provide a guideline for selecting appropriate co-occurrence criteria that corresponds to the purpose of the analysis. Additionally, we will perform similar experiments with several groups of customers that are segmented by each customer's average duration between orders. By this experiment, we attempt to discover the relationship between the optimal co-occurrence criteria and the customer's average duration between orders. Finally, by a series of experiments, we expect that we can provide basic guidelines for developing customized recommendation systems.