K-means clustering algorithm is a partitional clustering algorithm that has been used widely
in many applications for traditional clustering due to its simplicity and low computational …

Partitional data clustering with K-means algorithm is the dividing of objects into smaller and
disjoint groups that has the most similarity with objects in a group and most dissimilarity from …

Clustering algorithms are developed as a powerful tool to analyze the massive amount of
data which are produced by modern applications. The main goal of these algorithms is to …

K 均值聚类, 对于非凸, 稀疏及模糊的非线性可分数据, 其聚类效果不佳. 针对此问题,
通过引入粒计算理论, 采用邻域粒化技术, 提出一种邻域粒K 均值聚类方法 …

The discrimination of water–land waveforms is a critical step in the processing of airborne
topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser …

The performance of data clustering algorithms is mainly dependent on their ability to
balance between the exploration and exploitation of the search process. Although some …

Optimization is increasingly important due to its application in the real-world problems. The
imperialist competitive algorithm (ICA) is a successful optimization algorithm in many …

摘要向空间扩展维度的通信模型是进一步提升无线通信容量的重要研究方向. 然而,
空间维度上的正交信道构建主要还存在两方面的理论问题: 一是病态问题; 二是稀疏问题 …

In this paper, an improved imperialist competitive algorithm called I-ICA is proposed for
solving constrained optimisation problems. In I-ICA, the use of differential evolution …

The goal of optimizing the best acceptable answer is according to the limitations and needs
of the problem. For a problem, there are several different answers that are defined to …