کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
427882 | 686571 | 2011 | 7 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: HC_AB: A new heuristic clustering algorithm based on Approximate Backbone HC_AB: A new heuristic clustering algorithm based on Approximate Backbone](/preview/png/427882.png)
Clustering is an important research area with numerous applications in pattern recognition, machine learning, and data mining. Since the clustering problem on numeric data sets can be formulated as a typical combinatorial optimization problem, many researches have addressed the design of heuristic algorithms for finding sub-optimal solutions in a reasonable period of time. However, most of the heuristic clustering algorithms suffer from the problem of being sensitive to the initialization and do not guarantee the high quality results. Recently, Approximate Backbone (AB), i.e., the commonly shared intersection of several sub-optimal solutions, has been proposed to address the sensitivity problem of initialization. In this paper, we aim to introduce the AB into heuristic clustering to overcome the initialization sensitivity of conventional heuristic clustering algorithms. The main advantage of the proposed method is the capability of restricting the initial search space around the optimal result by defining the AB, and in turn, reducing the impact of initialization on clustering, eventually improving the performance of heuristic clustering. Experiments on synthetic and real world data sets are performed to validate the effectiveness of the proposed approach in comparison to three conventional heuristic clustering algorithms and three other algorithms with improvement on initialization.
► We define AB as the new search start point for heuristic clustering.
► We define scale and purity to guide the selection of AB.
► We propose a new algorithm to address the heuristic clustering.
► We evaluate the efficiency and effectiveness of the proposed approach.
Journal: Information Processing Letters - Volume 111, Issue 17, 15 September 2011, Pages 857–863