Due to its simplicity, versatility and the diversity of applications to which it can be applied, k-
means is one of the well-known algorithms for clustering data. The foundation of this …

Clustering analysis has the very broad applications on data analysis, such as data mining,
machine learning, and information retrieval. In practice, most of clustering algorithms suffer …

As a significant component of the intelligent transportation system, forecasting bus
passenger flows plays a key role in resource allocation, network planning, and frequency …

The Montney tight formation, located in Western Canadian Sedimentary Basin (WCSB), is
becoming an important component of hydrocarbon sources in Canada. Horizontal drilling …

Modified sequential k‐means clustering concerns ak‐means clustering problem in which the
clustering machine utilizes output similarity in addition. While conventional clustering …

Most existing clustering algorithms are slow for dividing a large dataset into a large number
of clusters. In this paper, we propose a truncated FCM algorithm to address this problem …

聚类是一种典型且重要的数据挖掘方法, 但现有聚类算法大多需要人为指定聚类的数量,
并且聚类结果对参数敏感. 针对上述不足, 本文提出一种基于子博弈完美均衡的启发式聚类算法 …

In view of intelligent Minkowski metric Weighted K-means (iMWK) sensitive to feature
weighting, a novel clustering technique called intelligent Minkowski metric feature weights …

As an effective clustering method, Affinity Propagation (AP) has received extensive
attentions. But its applications are seriously limited by two major deficiencies. Firstly, the …

Querying high quality constraints for clustering is a difficult task in clustering research due to
the lack of an appropriate criterion for measuring the quality of constraints. In this paper, we …