Correlation study is at the heart of time-varying multivariate volume data analysis and visualization. In this paper, we study hierarchical clustering of volumetric samples based on the similarity of their correlation relation. Samples are selected from a time-varying multivariate climate data set according to knowledge provided by the domain experts. We present three different hierarchical clu...

Last time, we introduced the task of hierarchical clustering, in which we aim to produce nested clusterings that reflect the similarity between clusters. This contrasts sharply with our former discussion of “flat” or structureless clustering methods like k-means which do not model relationships between clusters. In this lecture, we will continue our discussion of the standard model-free approac...

Interval-valued data can find their practical applications in such situations as recording monthlyinterval temperatures at meteorological stations, daily interval stock prices, etc. The primary objectiveof the presented paper is to compare three different methods of fuzzy clustering for interval-valuedsymbolic data, i.e.: fuzzy c-means clustering, adaptive fuzzy c-means clustering a...

The crosslingual link detection problem calls for identifying news articles in multiple languages that report on the same news event. This paper presents a novel approach based on constrained clustering. We discuss a general way for constrained clustering using a recent, graph-based clustering framework called correlation clustering. We introduce a correlation clustering implementation that fea...

Figure 15.1: k=3 clusters with red points chosen as facilities. Consider a situation where we have n point locations and we wish to place k facilities among these points to provide some service. It is desirable to have these facilities close to the points they are serving, but the notion of “close” can have different interpretations. The k-means problem seeks to place k facilities so as to mini...

Conventional clustering means classifying the given data objects as exclusive subsets (clusters).That means we can discriminate clearly whether an object belongs to a cluster or not. However such a partition is insufficient to represent many real situations. Therefore a fuzzy clustering method is offered to construct clusters with uncertain boundaries and allows that one object belongs to overl...

with rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. because o...

We propose a new algorithm for consensus clustering, FCAConsensus, based on Formal Concept Analysis. As the input, the algorithm takes T partitions of a certain set of objects obtained by k-means algorithm after T runs from different initialisations. The resulting consensus partition is extracted from an antichain of the concept lattice built on a formal context objects× classes, where the clas...

We develop a consensus clustering framework proposed three decades ago in Russia and experimentally demonstrate that our least squares consensus clustering algorithm consistently outperforms several recent consensus clustering methods. keywords: consensus clustering, ensemble clustering, least squares

While classification rules are essential in supervised classification methods, they are not noticed well in methods of clustering. Nevertheless, some clustering techniques have clear rules of classification, while they are not obvious in other methods. This paper discusses classification rules or classification functions in the former class including K-means, fuzzy c-means, and the mixture of d...