Data analysis plays an indispensable role for understanding various phenomena. Cluster
analysis, primitive exploration with little or no prior knowledge, consists of research …

We survey agglomerative hierarchical clustering algorithms and discuss efficient
implementations that are available in R and other software environments. We look at …

METHOD The algorithm requires as input a matrix of M points in N dimensions and a matrix
of K initial cluster centres in N dimensions. The number of points in cluster L is denoted by …

Clustering is the division of data into groups of similar objects. In clustering, some details are
disregarded in exchange for data simplification. Clustering can be viewed as a data …

Clustering algorithms have emerged as an alternative powerful meta-learning tool to
accurately analyze the massive volume of data generated by modern applications. In …

Liu has written a comprehensive text on Web mining, which consists of two parts. The first
part covers the data mining and machine learning foundations, where all the essential …

The monograph Data Clustering: Theory, Algorithms, and Applications was published in
2007. Starting with the common ground and knowledge for data clustering, the monograph …

This paper has two contributions. First, we introduce a clustering basic benchmark. Second,
we study the performance of k-means using this benchmark. Specifically, we measure how …

With the technology development in cyber physical systems and big data, there are huge
potential to apply them to achieve personalization and improve resource efficiency in …

Subspace clustering is an extension of traditional clustering that seeks to find clusters in
different subspaces within a dataset. Often in high dimensional data, many dimensions are …