디지털융복합연구 (Journal of Digital Convergence)

  • 제15권6호
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  • Pages.229-236
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  • 2017
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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RHadoop 플랫폼기반 CAWFP-Tree를 이용한 적응 빈발 패턴 알고리즘

Adaptive Frequent Pattern Algorithm using CAWFP-Tree based on RHadoop Platform

  • 박인규 (중부대학교 게임소프트웨어학과)
  • Park, In-Kyu (Dept. of Game Software, College of Engineering Joongbu University)
  • 투고 : 2017.05.01
  • 심사 : 2017.06.20
  • 발행 : 2017.06.28
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⟨ 이전 논문 다음 논문 ⟩

초록

효율적인 빈발 패턴 알고리즘은 연관 규칙 마이닝이나 융복합을 위한 마이닝 과정에서 필수적인 요소이며 많은 활용성을 가지고 있다. 패턴 마이닝을 위한 많은 모델들이 빈발 패턴에 관한 정보를 추출하여 FP-트리를 이용하여 저장하고 있다. 본 논문에서는 항목들의 무게중심을 이용한 새로운 빈발 패턴 알고리즘(CAWFP-Growth)을 제안하여 항목들이 가지는 가중치와 빈도수를 같이 고려하여 항목간의 중심을 계산하여 기존의 FP-Growth 알고리즘의 효율성을 향상시킨다. 제안한 방법은 하향 폐쇄의 성질을 유지하기 위한 기존의 전역적 최대치 가중치 지지도를 필요로 하지 않기 때문에 자연히 빈발 패턴의 탐색시간이 줄어들고 정보의 손실을 줄일 수 있다. 실험결과를 통하여 제안된 알고리즘이 기존의 동적가중치를 이용하는 다른 방법과 비교해볼 때, 항목들의 무게중심이 빈발패턴의 정확한 정보를 유지하고 FP-트리의 처리시간을 줄여주기 때문에 제안한 방법의 중요성을 보이고 있다 또한 가상 분산모드에서 맵리듀스 프레임을 기반으로 빅데이터를 모델링하고 향후 완전분산 모드에서 제안한 알고리즘의 모델링이 필요하다.

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.

키워드

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