• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.41 no.7
• /
• pp.768-774
• /
• 2016

This paper deals with a method to recommend the combination of items as a group according to similarity to handle application area such as fashion and cooking, while the previous methods recommend single item such as a book, music or movie. Collaborative filtering is a method to recommend an item selected by users with similar tendency based on similarity between users. In this paper, the proposed method generates a set of frequent items based on collaborative filtering and association rules and recommends a group by similarity between groups. To show the validity of the proposed method, experiments are performed with purchase data collected from e-commerce for four months.

• Journal of Korean Society of Transportation
• /
• v.19 no.1
• /
• pp.139-148
• /
• 2001

본 연구는 교통사고 야기 자들의 운전정밀적성검사기록과 해당 교통사고기록을 비교하여 특정 운전적성상의 결손이 교통사고와 특정유형의 교통사고에 미치는 영향을 계량적으로 분석하는 것을 목적으로 하였다. 본 연구를 위하여 우리나라에서 가장 빈발하는 인적요인에 의한 교통사고 유형 중에서 교통상충이 빈발하는 지점에서 발생하는 차-대-차 사고를 조사분석대상으로 삼았다. 이와 같이 분석대상사고를 선정한 이유는 교통상충이 교통사고로 발전하는 과정에서 사고 제1당사자의 운전적성의 역할을 파악하고자하는 목적에서이다. 따라서 본 연구의 대상이 되는 교통사고 유형을 1. 교차로 진입부에서의 추돌사고, 2. 교차로내 충추돌사고, 3. 단일로상의 추돌사고로 선정하였다. 판별력이 의문시되는 4개 항목을 제외한 조사분석결과에서 하나의 항목을 제외하고 사고 야기자와 일반인의 운전적성상에 통계적으로 분명한 차이가 있으며 각 항목의 결손자 집단에서의 사고자 비율이 일반운전자 집단에 비해 교통사고를 경험한 확률이 2배 내지 4배 가량 높았다. 또한 특정 유형 사고 야기자와 사고 야기자 전체, 일반운전자 집단간 비교에서도 항목별 결손율이 분명한 다른 형태를 갖고 있다는 사실을 확인했으며, 특정유형의 결손(조합)자 집합에서 특정 교통사고 유형을 야기시켰을 확률이 일반인 집단에 비해 많게는 13배 적게는 3, 4배 정도 높다고 추정되었다.

• Journal of KIISE:Databases
• /
• v.31 no.3
• /
• pp.287-296
• /
• 2004

Recently, various location-based services are becoming very popular in mobile environments. In this paper, we propose a new concept of a frequent item set, called “associative class set”, for supporting the location-based service which uses a large quantity of a spatial database in mobile computing environments, and then present a new method for efficiently generating the associative class set. The associative class set is generated with considering the temporal relation of queries, the spatial distance of required objects, and access patterns of users. The result of our research can play a fundamental role in efficiently supporting location-based services and in overcoming the limitation of mobile environments. The associative class set can be applied by a recommendation system of a geographic information system in mobile computing environments, mobile advertisement, city development planning, and client cache police of mobile users.

• Proceedings of the Korean Information Science Society Conference
• /
• 2012.06c
• /
• pp.134-136
• /
• 2012

u-City 환경에서 사용자 맞춤형 국토정보를 제공하기 위해 대용량의 데이터를 효과적으로 분석할 수 있는 데이터마이닝 기법이 적용되고 있다. 따라서 이 논문에서는 데이터마이닝 기법 중 연관분류기법을 적용하여 사용자 맞춤형 관광명소 추천 시스템을 개발하였다. 특히, CPFP-tree를 이용하여 빈발항목집합 탐사에 대한 시간을 단축하였으며, 연관분류를 통해 보다 높은 정확도로 결과를 예측 및 분류할 수 있게 하였다. 제시한 시스템은 공간정보에 대해 사용자 맞춤 서비스를 제공할 수 있음을 보였으며, 다양한 시나리오 적용을 통해 맞춤형 국토정보화 기술의 기반이 될 수 있다.

• Journal of the Korea Society of Computer and Information
• /
• v.15 no.11
• /
• pp.205-213
• /
• 2010

This study uses the FP-tree algorithm, one of the mining techniques. This study is an attempt to suggest a new recommended system using a modified FP-tree algorithm which yields an association rule based on frequent 2-itemsets extracted from the transaction database. The modified recommended system consists of a pre-processing module, a learning module, a recommendation module and an evaluation module. The study first makes an assessment of the modified recommended system with respect to the precision rate, recall rate, F-measure, success rate, and recommending time. Then, the efficiency of the system is compared against other recommended systems utilizing the sequential pattern mining. When compared with other recommended systems utilizing the sequential pattern mining, the modified recommended system exhibits 5 times more efficiency in learning, and 20% improvement in the recommending capacity. This result proves that the modified system has more validity than recommended systems utilizing the sequential pattern mining.

• Proceedings of the Korean Information Science Society Conference
• /
• 2001.04b
• /
• pp.91-93
• /
• 2001

최근 지놈(Genome) 프로젝트를 통해 핵산, 단백질 서열 정보가 밝혀짐에 따라 분자 수준의 유전자 정보를 다루는 기법들이 활발히 연구되면서 방대한 서열 정보를 데이터 베이스화하고, 부족하기 위한 효과적인 도구와 컴퓨터 알고리즘의 개발을 필요로 하고 있다. 본 논문에서는 여러 단백질에 공통적으로 존재하는 서열 정보간에 존재하는 연관성을 탐사하기 위한 서열 연관 규칙 알고리즘을 제안한다. 원자 항목을 취급하였던 기존 알고리즘과는 달리 중복을 반영해야 하는 서열 데이터의 특성을 고려하여야 한다. 실험을 단백질 서열 데이터를 대상으로 수행하였다. 먼저 여러 서열에 빈발하게 발생하는 부 서열 집합을 찾고, 부 서열 집합들간에 존재하는 관련성을 탐사한다. 본 연구의 결과는 탐사된 규칙으로부터 다른 단백질의 구조와 기능을 예측할 수 있고, 이 정보는 필요로 하는 생물학적 분석을 방향을 제시할 것이다. 이는 생물학적 실험 대상의 후부조합을 최소화함으로써 많은 시간과 노력 비용을 절감할 수 있다.

• Journal of Software Assessment and Valuation
• /
• v.16 no.1
• /
• pp.81-88
• /
• 2020

Various methods of differentiating and servicing digital content for individual users have been studied. Searching for association rules is a very useful way to discover individual preferences in digital content services. The Apriori algorithm is useful as an association rule extractor using frequent itemsets. However, the Apriori algorithm is not suitable for application to an actual content service because it considers only the reference count of each content. In this paper, we propose a new algorithm based on the Apriori that searches association rules by using purchase history and usage-time for each item. The proposed algorithm utilizes the usage time with the weight value according to purchase items. Thus, it is possible to extract the exact preference of the actual user. We implement the proposed algorithm and verify the performance through the actual data presented in the actual content service system.

• The KIPS Transactions:PartD
• /
• v.8D no.4
• /
• pp.339-346
• /
• 2001

A problem for mining association rules under the interactive environments is to mine repeatedly association rules with the different minimum support. This problem includes all subproblems except on the facts that mine repeatedly association rules with the s믇 database. This paper proposed the efficient algorithms to improve the performance by using the information of the candidate large itemsets which calculate the previous association rules. The proposed algorithms were compared with the conventional algorithm with respect to the execution time. The comparisons show that the proposed algorithms achieve 10∼30% more gain than the conventional algorithm.

• The KIPS Transactions:PartD
• /
• v.19D no.1
• /
• pp.1-14
• /
• 2012

As the size of the data warehouse is large, the selection of indices on the data warehouse affects the efficiency of the query processing of the data warehouse. Indices induce the lower query processing cost, but they occupy the large storage areas and induce the index maintenance cost which are accompanied by database updates. The bitmap join indices are well applied when we optimize the star join queries which join a fact table and many dimension tables and the selection on dimension tables in data warehouses. Though the bitmap join indices with the binary representations induce the lower storage cost, the task to select the indexing attributes among the huge candidate attributes which are generated is difficult. The processes of index selection are to reduce the number of candidate attributes to be indexed and then select the indexing attributes. In this paper on bitmap join index selection problem we reduce the number of candidate attributes by the data mining techniques. Compared to the existing techniques which reduce the number of candidate attributes by the frequencies of attributes we consider the frequencies of attributes and the size of dimension tables and the size of the tuples of the dimension tables and the page size of disk. We use the mining of the frequent itemsets as mining techniques and reduce the great number of candidate attributes. We make the bitmap join indices which have the least costs and the least storage area adapted to storage constraints by using the cost functions applied to the bitmap join indices of the candidate attributes. We compare the existing techniques and ours and analyze them in order to evaluate the efficiencies of ours.

• Journal of KIISE:Databases
• /
• v.35 no.3
• /
• pp.199-207
• /
• 2008

The FP-tree(Frequency Pattern Tree) mining association rules algorithm was proposed to improve mining performance by reducing DB scan overhead dramatically, and it is recognized that the performance of it is better than that of any other algorithms based on different approaches. But the FP-tree algorithm needs a few more memory because it has to store all transactions including frequent itemsets of the DB. This paper implements a FP-tree algorithm on a general purpose UNK system and compares it with the DHP(Direct Hashing and Pruning) algorithm which uses hash tree and direct hash table from the point of memory usage and execution time. The results show surprisingly that the FP-tree algorithm is poor than the DHP algorithm in some cases even if the system memory is sufficient for the FP-tree. The characteristics of the test data are as follows. The site of DB is look, the number of total items is $1K{\sim}7K$, avenrage length of transactions is $5{\sim}10$, avergage size of maximal frequent itemsets is $2{\sim}12$(these are typical attributes of data for large-scale convenience stores).