• Journal of Digital Convergence
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• v.15 no.6
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• pp.229-236
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• 2017

An efficient frequent pattern algorithm is essential for mining association rules as well as many other mining tasks for convergence with its application spread over a very broad spectrum. Models for mining pattern have been proposed using a FP-tree for storing compressed information about frequent patterns. In this paper, we propose a centroid frequent pattern growth algorithm which we called "CAWFP-Growth" that enhances he FP-Growth algorithm by making the center of weights and frequencies for the itemsets. Because the conventional constraint of maximum weighted support is not necessary to maintain the downward closure property, it is more likely to reduce the search time and the information loss of the frequent patterns. The experimental results show that the proposed algorithm achieves better performance than other algorithms without scarifying the accuracy and increasing the processing time via the centroid of the items. The MapReduce framework model is provided to handle large amounts of data via a pseudo-distributed computing environment. In addition, the modeling of the proposed algorithm is required in the fully distributed mode.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.33-46
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• 2011

Since an e-mail has been an important mean of communication and information sharing, there have been much effort to classify e-mails efficiently by their contents. An e-mail has various forms in length and style, and words used in an e-mail are usually irregular. In addition, the criteria of an e-mail classification are subjective. As a result, it is quite difficult for the conventional text classification technique to be adapted to an e-mail classification efficiently. An e-mail classification technique in a commercial e-mail program uses a simple text filtering technique in an e-mail client. In the previous studies on automatic classification of an e-mail, the Naive Bayesian technique based on the probability has been used to improve the classification accuracy, and most of them are on an e-mail in English. This paper proposes the personalized recommendation technique of an email in Korean using a data mining technique of frequent patterns. The proposed technique consists of two phases such as the pre-processing of e-mails in an e-mail folder and the generating a profile for the e-mail folder. The generated profile is used for an e-mail to be classified into the most appropriate e-mail folder by the subjective criteria. The e-mail classification system is also implemented, which adapts the proposed technique.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1593-1601
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• 2017

In a big data platform, association rule mining applications could bring some benefits. For instance, in a agricultural big data platform, the association rule mining application could recommend specific products for farmers to grow, which could increase income. The key process of the association rule mining is the frequent itemsets mining, which finds sets of products accompanying together frequently. Former researches about this issue, e.g. Apriori, are not satisfying enough because huge possible sets can cause memory to be overloaded. In order to deal with it, SON algorithm has been proposed, which divides the considered set into many smaller ones and handles them sequently. But in a single machine, SON algorithm cause heavy time consuming. In this paper, we present a method to find association rules in our Hadoop based big data platform, by parallelling SON algorithm. The entire process of association rule mining including pre-processing, SON algorithm based frequent itemset mining, and association rule finding is implemented on Hadoop based big data platform. Through the experiment with real dataset, it is conformed that the proposed method outperforms a brute force method.

• Asia pacific journal of information systems
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• v.18 no.2
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• pp.95-114
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• 2008

Recent interests in data mining result from the expansion of the amount of business data and the growing business needs for extracting valuable knowledge from the data and then utilizing it for decision making process. In particular, recent advances in association rule mining techniques enable us to acquire knowledge concerning sales patterns among individual items from the voluminous transactional data. Certainly, one of the major purposes of association rule mining is to utilize acquired knowledge in providing marketing strategies such as cross-selling, sales promotion, and shelf-space allocation. In spite of the potential applicability of association rule mining, unfortunately, it is not often the case that the marketing mix acquired from data mining leads to the realized profit. The main difficulty of mining-based profit realization can be found in the fact that tremendous numbers of patterns are discovered by the association rule mining. Due to the many patterns, data mining experts should perform additional mining of the results of initial mining in order to extract only actionable and profitable knowledge, which exhausts much time and costs. In the literature, a number of interestingness measures have been devised for estimating discovered patterns. Most of the measures can be directly calculated from what is known as a contingency table, which summarizes the sales frequencies of exclusive items or itemsets. A contingency table can provide brief insights into the relationship between two or more itemsets of concern. However, it is important to note that some useful information concerning sales transactions may be lost when a contingency table is constructed. For instance, information regarding the size of each market basket(i.e., the number of items in each transaction) cannot be described in a contingency table. It is natural that a larger basket has a tendency to consist of more sales patterns. Therefore, if two itemsets are sold together in a very large basket, it can be expected that the basket contains two or more patterns and that the two itemsets belong to mutually different patterns. Therefore, we should classify frequent itemset into two categories, inter-pattern co-occurrence and intra-pattern co-occurrence, and investigate the effect of the market basket size on the two categories. This notion implies that any interestingness measures for association rules should consider not only the total frequency of target itemsets but also the size of each basket. There have been many attempts on analyzing various interestingness measures in the literature. Most of them have conducted qualitative comparison among various measures. The studies proposed desirable properties of interestingness measures and then surveyed how many properties are obeyed by each measure. However, relatively few attentions have been made on evaluating how well the patterns discovered by each measure are regarded to be valuable in the real world. In this paper, attempts are made to propose two notions regarding association rule measures. First, a quantitative criterion for estimating accuracy of association rule measures is presented. According to this criterion, a measure can be considered to be accurate if it assigns high scores to meaningful patterns that actually exist and low scores to arbitrary patterns that co-occur by coincidence. Next, complementary measures are presented to improve the accuracy of traditional association rule measures. By adopting the factor of market basket size, the devised measures attempt to discriminate the co-occurrence of itemsets in a small basket from another co-occurrence in a large basket. Intensive computer simulations under various workloads were performed in order to analyze the accuracy of various interestingness measures including traditional measures and the proposed measures.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.11a
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• pp.97-100
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• 2005

최근 유비쿼터스 컴퓨팅 및 인터넷 서비스에 대한 관심이 증대되면서, 대용량의 데이터에 내재되어 있는 정보를 빠른 시간 내에 처리하여 새로운 지식을 창출하려는 요구가 증가하고 있다. 데이터 마이닝 기법을 이용하여 데이터 스트림에서 빈발항목을 탐색하는 기존의 연구는 시간을 고려하지 않고 단순히 집계를 통하여 빈발항목을 탐색하기 때문에 정확성을 보장하지 못한다. 따라서 본 논문에서는 데이터 스트림에서 시간적 측면을 고려하여 상대적인 빈발항목을 탐색하기 위한 새로운 알고리즘을 제안하고자 한다. 논문에서 제안하는 알고리즘의 성능은 다양한 실험을 통해서 검증된다.

• Proceedings of the Korean Information Science Society Conference
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• 2005.11b
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• pp.145-147
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• 2005

최근 들어 저장장치의 발전과 네트워크의 발달로 인하여 대용량의 데이터가 매우 빠르게 증가되고 있다. 또한, 대용량의 데이터에 내재되어 있는 정보를 빠른 시간 내에 처리하여 새로운 지식을 창출하려는 요구가 증가하고 있다. 연속적이고 빠르게 증가하는 데이터를 지칭하는 데이터 스트림에서 데이터 마이닝 기법을 이용하여 시간이 흐름에 따라 변하고, 무한적으로 증가하는 데이터 스트림에서의 빈발항목을 찾는 연구가 활발하게 진행되고 있다. 하지만 기존의 연구들은 시간의 흐름에 따른 빈발항목 탐색방법을 적절히 제시하지 못하고 있으며 단지 집계를 이용하여 빈발항목을 탐색하고 있다. 본 논문에서는 데이터 스트림에서 시간적 측면을 고려하여 상대적인 빈발항목을 탐색하기 위한 새로운 알고리즘을 제안한다. 논문에서 제안하는 알고리즘의 성능은 다양한 실험을 통해서 검증된다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.7
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• pp.1327-1331
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• 2010

Association rules are very useful and interesting patterns for discovering preferences of each person in digital-content services. The Apriori algorithm is an influential algorithm for mining frequent itemsets for association rules. However, since this algorithm does not take into account reference times of each content as an important support factor, it cannot be used to extract associations among time-based data. This paper proposes an augmented Apriori algorithm discovers association rules using both frequencies and usage times of each item.

• Proceedings of the Korean Information Science Society Conference
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• 2003.10b
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• pp.10-12
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• 2003

대용량의 데이터베이스에서 최소지지도를 만족하는 항목들의 집합을 빈발 항목집합이라고 한다. 이전에 연구된 대부분의 빈발 항목집합 생성 알고리즘들은 후보 항목집합들을 생성하고 이들 중에서 조건을 만족하는 빈발 항목집합들을 생성하는 과정을 수행하였다. 그러나 이러한 알고리즘들은 모든 k(k$\geq$1)-빈발 항목집합들을 생성하기까지 k를 하나씩 증가하면서 반복적으로 수행되기 때문에 많은 컴퓨팅 시간을 필요로 한다. 본 논문에서는 후보 항목집합들을 생성하지 않고 빈발 항목집합들을 생성할 수 있는 DFG 알고리즘을 제안한다. 각각의 k-빈발 항목집합들에는 데이터베이스의 모든 정보들이 포함되어 있고 하나의 빈발 항목집합은 한 트랜잭션에 존재한다. 본 논문에서는 이러한 성질을 이용하여 먼저 2-빈발 항목집합들을 생성한다. 그리고 2-빈발 항목집합들에 존재하는 한 항목과 나머지 항목들에 대한 매트릭스를 구성하여 최소지지도를 만족하는 빈발 항목집합들을 생성하게 된다. 제안하는 알고리즘은 불필요한 후보 항목집합들을 생성하지 않고 한 번의 데이터베이스 스캔만으로 빈발 항목집합들을 생성할 수 있다.

• Proceedings of the Korean Information Science Society Conference
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• 2002.10c
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• pp.259-261
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• 2002

데이터마이닝 분야에서 빈발항목집합 탐사에 관한 연구는 활발히 진행되어 왔지만 여전히 많은 메모리 공간과 시간을 필요로 한다. 특히 apriori 알고리즘에 기반한 방법들은 긴 패턴이 생성될수록 지수적으로 시간과 공간이 증가한다. 최근에 발표된 fp-growth는 일반적인 데이터 집합에서 우수한 성능을 보이나 희소 데이터 집합에서 효율적인 성능을 보여주지 못한다. 본 논문에서는 길이가 2인 빈발항목집합 L2에 기반한 L2-tree 구조를 제안한다. 또한 L2-tree에서 빈발항목집합을 탐사하는 L2-traverse 알고리즘을 제안한다. L2-tree는 L2를 기반으로 하기 때문에 L2가 상대적으로 적은 희소 데이터 집합 환경에서 적은 메모리 공간을 사용하게 된다. L2-traverse 알고리즘은 별도의 추출 데이터베이스를 생성하는 FP-growth와 달리 단순히 L2-tree를 오직 한번의 깊이 우선 탐사를 통해 빈발항목집합을 찾는다. 최적화 기법으로써 길이가 3인 빈발항목집합 L3가 되지 않는 L2 패턴들을 미리 제거하는 방법으로 C3-traverse 알고리즘을 제안하며 실험을 통해 기존 알고리즘과 비교 검증한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2009.04a
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• pp.375-376
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• 2009

트리기반 빈발 항목 집합 알고리즘들은 전체적으로 밀집 빈발 항목 집합에는 효율적이고 빠르게 빈발 항목 집합을 탐색하나 희소 빈발 항목 집합에는 효율적이지 않고 빈발 항목 집합을 빠르게 탐색하지 못한다. 반면에 배열기반 빈발 항목 집합 알고리즘은 희소 빈발 항목 집합에 효율적이고 빠르게 빈발 항목 집합을 탐색하나 밀집 빈발 항목 집합에는 효율적이지 않고 빈발 항목 집합을 빠르게 탐색하지 못한다. 밀집 및 희소 빈발 항목 집합 모두 효율적으로 빈발 항목 집합을 탐색 하고자 하는 시도가 있었으나 두 가지 종류의 알고리즘을 동시에 사용하므로 각각의 알고리즘을 사용할 정확한 기준 제시가 어렵고, 두 가지 알고리즘의 단점을 내포한다. 따라서 본 논문에서는 단일 알고리즘을 사용하여 밀집 빈발 항목 집합 및 희소 빈발 항목 집합 모두에 대해 작은 메모리 공간을 사용하면서도 효율적이고 빠르게 빈발 항목 집합을 탐색할 수 있는 CPFP-Tree라는 새로운 자료구조와 탐색 방법을 제안한다.