As an unsupervised learning method, clustering does not need to know prior knowledge of
the datasets in advance. How determining the optimal number of clusters becomes an …

Today's data mostly does not include the knowledge of cluster number. Therefore, it is not
possible to use conventional clustering approaches to partition today's data, ie, it is …

This is the first book to take a truly comprehensive look at clustering. It begins with an
introduction to cluster analysis and goes on to explore: proximity measures; hierarchical …

Aiming at the defects of the sparrow search algorithm (SSA), such as a deficient optimization
accuracy and low search efficiency, the sparrow search algorithm based on quantum …

The massive growth of data in recent years has led challenges in data mining and machine
learning tasks. One of the major challenges is the selection of relevant features from the …

Seeking multiple optima simultaneously, which multimodal optimization aims at, has
attracted increasing attention but remains challenging. Taking advantage of ant colony …

In this paper, a neighborhood mutation strategy is proposed and integrated with various
niching differential evolution (DE) algorithms to solve multimodal optimization problems …

Clustering validation has long been recognized as one of the vital issues essential to the
success of clustering applications. In general, clustering validation can be categorized into …

In cluster analysis, a fundamental problem is to determine the best estimate of the number of
clusters; this is known as the automatic clustering problem. Because of lack of prior domain …

Taking the advantage of estimation of distribution algorithms (EDAs) in preserving high
diversity, this paper proposes a multimodal EDA. Integrated with clustering strategies for …