• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.454-457
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• 2005

Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and/or long patterns. And calendar based on temporal association rules proposes the discovery of association rules along with their temporal patterns in terms of calendar schemas, but this approach is also adopt an Apriori-like candidate set generation. In this paper, we propose an efficient temporal frequent pattern mining using TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (1) this method separates many partitions by according to maximum size domain and only scans the transaction once for reducing the I/O cost. (2) This method maintains all of transactions using FP-trees. (3) We only have the FP-trees of I-star pattern and other star pattern nodes only link them step by step for efficient mining and the saving memory. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the Apriori algorithm and also faster than calendar based on temporal frequent pattern mining methods.

• Journal of Korea Spatial Information System Society
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• v.8 no.1 s.16
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• pp.27-35
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• 2006

As the increasing interest about geographical information, such researches and applied fields become wide. OGC(Open GIS Consortium) developed GML(Geography Markup Language) which is adopted XML(extensible Markup Language) in GIS field. In various applied field, GML is used and studied continuously. This paper try to find out the meaningful rules using Apriori algorithm from GML documents, one of the data mining techniques which is studied based on existing XML documents There are two ways to find out the rules. One is the way that find out the related rules as extracting the content in GML documents, the other find out the related rules based on used tags and attributes. This paper describes searching the rules through two ways and shows the system adopted two ways.

• Journal of applied mathematics & informatics
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• v.6 no.1
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• pp.51-64
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• 1999

We give a new characterization of the solutions set of the general (inconsistent) linear least-squares problem using the set of linit-points of an extended version of the classical Daczmarz's pro-jections method. We also obtain a "step error reduction formula" which in some cases can give us apriori information about the con-vergence properties of the algorithm. Some numerical experiments with our algorithm and comparisons between it and others existent in the literature are made in the last section of the paper.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.277-284
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• 2021

Recently, the smart home environment is expected to be a platform that collects, integrates, and utilizes various data through convergence with wireless information and communication technology. In fact, the number of smart devices with various sensors is increasing inside smart homes. The amount of data that needs to be processed by the increased number of smart devices is also increasing, and big data processing systems are actively being introduced to handle it effectively. However, traditional big data processing systems have all requests directed to cluster drivers before they are allocated to distributed nodes, leading to reduced cluster-wide performance sharing as cluster drivers managing segmentation tasks become bottlenecks. In particular, there is a greater delay rate on smart home devices that constantly request small data processing. Thus, in this paper, we design a Apriori-based big data system for effective data processing in smart home environments where frequent requests occur at the same time. According to the performance evaluation results of the proposed system, the data processing time was reduced by up to 38.6% from at least 19.2% compared to the existing system. The reason for this result is related to the type of data being measured. Because the amount of data collected in a smart home environment is large, the use of cache servers plays a major role in data processing, and association analysis with Apriori algorithms stores highly relevant sensor data in the cache.

• Journal of KIISE:Software and Applications
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• v.26 no.3
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• pp.343-352
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• 1999

본 논문에서는 대용량 데이터베이스에서 효율적인 연관 규칙 탐사에 대한 알고리즘을 제안하였다. 제안하는 알고리즘은 복합 해쉬 트리를 사용하여 해쉬 트리 탐색 비용과 데이터베이스 스캔 비용을 동시에 줄임으로서 성능을 향상시켰다. 복합 해쉬 트리는 같은 크기의 항목집합들 대신에 크기가 다른 여러 항목집합을 하나의 해쉬 트리로 구성한다. 복합 해쉬 트리의 유용성을 보이기 위하여 제안한 알고리즘은 잘 알려져 있는 Apriori, DHP 방밥과 수행 시간 측면에서 성능 비교를 하였다. 그 결과 대부분의 최소 지지도에서제안한 알고리즘이 Apriori, DHP 방법보다 우수하게 나타났으며, 최소 지지도가 0.5% 이하인 경우 DHP 방법에 비하여 약 30%의 이득 향상이 있었다.

• The Journal of Korean Association of Computer Education
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• v.9 no.1
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• pp.71-80
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• 2006

As e-commerce developing rapidly, it is becoming a research focus about how to find customer's behavior patterns and realize commerce intelligence by use of Web mining technology. One of the most successful and widely used technologies for building personalization and goods recommendation system is collaborating filtering. However, collaborative filtering have serious data sparsity problem. Traditional association rule does not consider user's interests or preferences to provide a user with specific personalized service.In this paper, we propose an goods recommendation system, which is integrated an collaborative filtering algorithm with item-to-item corelation and an improved Apriori algorithm. This system has user's interests or preferences ro provide a user with specific personalized service.

• Annual Conference on Human and Language Technology
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• 2010.10a
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• pp.3-8
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• 2010

본 연구에서는 일련의 연구에서 수집된 영작문 오류 유형의 정제된 자료를 토대로 연관 규칙을 생성하고, 학습을 통해서 효용성이 검증된 연관 규칙을 활용해서 영작문 데이터의 형태 통사 오류를 자동으로 탐지한다. 영작문 데이터에서 형태 통사 오류를 찾아내는 작업은 많은 시간과 자원이 소요되는 작업이므로 자동화가 필수적이다. 기존의 연구들이 통계적 모델을 활용한 어휘적 오류에 치중하거나 언어 이론적 틀에 근거한 통사 처리에 집중하는 반면에, 본 연구는 데이터 마이닝을 통해서 정제된 데이터에서 연관 규칙을 생성하고 이를 검증한 후 형태 통사 오류를 감지한다. 이전 연구들에서는 이론적 틀에 맞추어진 규칙 생성이나 언어 모델 생성을 위한 대량의 코퍼스 데이터와 같은 다량의 지식 베이스 생성이 필수적인데, 본 연구는 적은 양의 정제된 데이터를 활용한다. 영작문 오류 유형의 형태 통사 연관 규칙을 생성하기 위해서 Apriori 알고리즘을 활용하였다. 알고리즘을 통해서 생성된 연관 규칙 중 잘못된 규칙이 생성될 가능성이 있으므로, 상관성 검정, 코사인 유사도와 같은 규칙 효용성의 통계적 검증을 활용해서 타당한 규칙만을 학습하였다. 이를 통해서 축적된 연관 규칙들을 영작문 오류를 자동으로 탐지하는 실험에 활용하였다.

• Journal of the Korea Society for Simulation
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• v.19 no.4
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• pp.59-66
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• 2010

We present a personalized clothing recommender system - one that mines association rules from transaction described in ontologies and infers a recommendation from the rules. The recommender system can forecast frequently changing trends of clothing using the Onto-Apriori algorithm, and it makes appropriate recommendations for each users possible through the inference marked as meta nodes. We simulates the rule generator and the inferential search engine of the system with focus on accuracy and efficiency, and our results validate the system.

• Convergence Security Journal
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• v.12 no.2
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• pp.61-70
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• 2012

The objective of this study is to achieve the most optimal data mining for effective analysis of false alarm event on mechanical security system. To perform this, this study searches the cause of false alarm and suggests the data conversion and analysis methods to apply to several algorithm of WEKA, which is a data mining program, based on statistical data for the number of case on movement by false alarm, false alarm rate and cause of false alarm. Analysis methods are used to estimate false alarm and set more effective reaction for false alarm by applying several algorithm. To use the suitable data for effective analysis of false alarm event on mechanical security analysis this study uses Decision Tree, Naive Bayes, BayesNet Apriori and J48Tree algorithm, and applies the algorithm by deducting the highest value.

• The KIPS Transactions:PartD
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• v.13D no.5 s.108
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• pp.651-660
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• 2006

Algorithms for mining association rules based on the Apriori algorithm use the hash tree data structure for storing and counting supports of the candidate frequent itemsets and the most part of the execution time is consumed for searching in the hash tree. The DHP(Direct Hashing and Pruning) algorithm makes efforts to reduce the number of the candidate frequent itemsets to save searching time in the hash tree. For this purpose, the DHP algorithm does preparative simple counting supports of the candidate frequent itemsets. At this time, the DHP algorithm uses the direct hash table to reduce the overhead of the preparative counting supports. This paper proposes and evaluates an efficient hashing mechanism for the direct hash table $H_2$ which is for pruning in phase 2 and the hash tree $C_k$, which is for counting supports of the candidate frequent itemsets in all phases. The results showed that the performance improvement due to the proposed hashing mechanism was 82.2% on the maximum and 18.5% on the average compared to the conventional method using a simple mod operation.