디지털융복합연구 (Journal of Digital Convergence)

  • 제20권2호
  • /
  • Pages.217-223
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  • 2022
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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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온라인 데이터 스트림에서의 동적 부분 공간 클러스터링 기법

Dynamic Subspace Clustering for Online Data Streams

  • 박남훈 (안양대학교 융합소프트웨어학과)
  • Park, Nam Hun (Dept. of Convergence Software, Anyang University)
  • 투고 : 2021.11.16
  • 심사 : 2022.02.20
  • 발행 : 2022.02.28
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초록

온라인 데이터 스트림에 대한 부분 공간 클러스터링은 데이터 공간 차원의 모든 부분 집합을 검사해야 하므로 많은 양의 메모리 자원을 필요로 한다. 유한한 메모리 공간에서 데이터 스트림에 대한 클러스터들의 지속적인 변화를 추적하기 위해 본 논문에서는 메모리 자원을 효과적으로 사용하는 격자기반 부분 공간 클러스터링 알고리즘을 제안한다. n차원 데이터 스트림이 주어지면 각 차원 데이터 공간에 있는 데이터 항목의 분포 정보를 격자셀 리스트에 의해 모니터링 된다. 첫번째 레벨의 격자셀 목록에서 데이터 항목의 빈도가 높아 단위 격자셀이 되면 해당 격자셀로부터 모든 가능한 부분 공간의 클러스터를 찾기 위해 다음 레벨의 격자셀 리스트를 자식 노드로 생성한다. 이와 같이 최대 다차원 n레벨의 격자셀 부분 공간 트리가 구성되고, k차원의 부분 공간 클러스터는 부분 공간 격자셀 트리의 k레벨에서 찾을 수 있다. 실험을 통해서 제안하는 방법이 기존 방법만큼 정확도를 유지하면서, 밀집 공간만 확장하여 컴퓨팅 자원을 보다 효율적으로 사용하는 것을 확인하였다.

Subspace clustering for online data streams requires a large amount of memory resources as all subsets of data dimensions must be examined. In order to track the continuous change of clusters for a data stream in a finite memory space, in this paper, we propose a grid-based subspace clustering algorithm that effectively uses memory resources. Given an n-dimensional data stream, the distribution information of data items in data space is monitored by a grid-cell list. When the frequency of data items in the grid-cell list of the first level is high and it becomes a unit grid-cell, the grid-cell list of the next level is created as a child node in order to find clusters of all possible subspaces from the grid-cell. In this way, a maximum n-level grid-cell subspace tree is constructed, and a k-dimensional subspace cluster can be found at the kth level of the subspace grid-cell tree. Through experiments, it was confirmed that the proposed method uses computing resources more efficiently by expanding only the dense space while maintaining the same accuracy as the existing method.

키워드

과제정보

This paper was supported by Anyang University Research Grant.

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