LeaDen-Stream: A Leader Density-Based Clustering Algorithm over Evolving Data Stream

• 作者: Amineh Amini Teh Ying Wah
• 来源期刊: 电脑和通信(英文)
• 学科: 医学
• 关键词: EVOLVING Data STREAMS Density-Based CLUSTERING Micro CLUSTER Mini-Micro CLUSTER
• 年，卷（期）: 2013,（5）
• 页码范围: 26-31
• 页数: 6页
• 分类号: R73

摘要

Clustering evolving data streams is important to be performed in a limited time with a reasonable quality. The existing micro clustering based methods do not consider the distribution of data points inside the micro cluster. We propose LeaDen-Stream (Leader Density-based clustering algorithm over evolving data Stream), a density-based clustering algorithm using leader clustering. The algorithm is based on a two-phase clustering. The online phase selects the proper mini-micro or micro-cluster leaders based on the distribution of data points in the micro clusters. Then, the leader centers are sent to the offline phase to form final clusters. In LeaDen-Stream, by carefully choosing between two kinds of micro leaders, we decrease time complexity of the clustering while maintaining the cluster quality. A pruning strategy is also used to filter out real data from noise by introducing dense and sparse mini-micro and micro-cluster leaders. Our performance study over a number of real and synthetic data sets demonstrates the effectiveness and efficiency of our method.