Constrained Data Clustering
Date Issued
2006
Date
2006
Author(s)
Dai, Bi-Ru
DOI
en-US
Abstract
Among various data mining capabilities, data clustering is a useful technique for group behavior investigation, and is helpful for many applications. Since data mining is an application dependent technology, the information involving domain knowledge is usually imposed on the mining systems as various constraints. In this dissertation, we address the problem of constrained clustering with numerical constraints, in which the constraint attribute values of any two data items in the same cluster are required to be within the corresponding constraint range. Several algorithms are proposed to solve such a clustering problem. It is noted that due to the intrinsic nature of the numerical constrained clustering, there is an order dependency on the process of attaining the clustering, which in many cases degrades the clustering results. In view of this, we devise a progressive constraint relaxation technique to remedy this drawback and improve the overall performance of clustering results.
In addition to clustering on static data sets, the problem of clustering multiple data streams is also addressed in this dissertation. We devise a Clustering on Demand framework, abbreviated as COD framework, to dynamically cluster multiple data streams. The COD framework consists of two phases, i.e., the online maintaining phase and the offline clustering phase. The online maintaining phase provides an efficient mechanism to maintain the summary hierarchies of the data streams with multiple resolutions. On the other hand, an adaptive clustering algorithm is devised for the offline phase to retrieve the approximations of the desired sub-streams from the summary hierarchies according to the clustering queries.
Finally, the concepts of constraints and data streams are combined and considered together. We devise a framework of Constrained Clustering for the Evolving Data Stream, abbreviated as CCDS framework, to cluster the data stream under the pairwise range constraint. Two phases are designed to maintain the data points and to generate clusters respectively.
Subjects
資料探勘
資料叢集
資料串流
Data Mining
Data Clustering
Data Stream
Type
thesis
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