This paper combines three exploratory data analysis methods, principal component methods, hierarchical clustering and partitioning, to enrich the description of the data. Principal component methods are used as preprocessing step for the clustering in order to denoise the data, transform categorical data in continuous ones or balanced groups of variables. The principal component representation ...

This dissertation explores methods for cluster analysis of acoustic data. Techniques developed are applied primarily to whale song, but the task is treated in as general a manner as possible. Three algorithms are presented, all built around hidden Markov models, respectively implementing partitional, agglomerative, and divisive clustering. Topology optimization through Bayesian model selection ...

In many applications, a set of objects can be represented by di erent points of view (universes). Beside numeric, ordinal and nominal features, objects may be represented using spatio-temporal information, sequences, and more complex structures (e.g., graphs). Learning from all these di erent spaces is challenging, since often di erent algorithms and metrics are needed. In the case of data clus...

The importance of unsupervised clustering and topic modeling is well recognized with everincreasing volumes of text data. In this paper, we propose a fast method for hierarchical clustering and topic modeling called HierNMF2. Our method is based on fast Rank-2 nonnegative matrix factorization (NMF) that performs binary clustering and an efficient node splitting rule. Further utilizing the final...

We consider the problem of clustering large document sets into disjoint groups or clusters. Our starting point is recent literature on effective clustering algorithms, specifically Principal Direction Divisive Partitioning (PDDP), proposed by Boley in [1] and Spherical k-Means (“S–kmeans” for short) proposed by Dhillon and Moda in [4]. In this paper we study and evaluate the performance of thes...

This paper proposes a new method for comparing clusterings both partitionally and geometrically. Our approach is motivated by the following observation: the vast majority of previous techniques for comparing clusterings are entirely partitional, i.e., they examine assignments of points in set theoretic terms after they have been partitioned. In doing so, these methods ignore the spatial layout ...

Text document clustering is a popular task for understanding and summarizing large document collections. Besides the need for efficiency, document clustering methods should produce clusters that are readily understandable as collections of documents relating to particular contexts or topics. Existing clustering methods often ignore term-document semantics while relying upon geometric similarity...

Clustering is a process of grouping same objects into a specified number of clusters. K-means and Kmedoids algorithms are the most popular partitional clustering techniques for large data sets. However, they are sensitive to random selection of initial centroids and are fall into local optimal solution. K-means++ algorithm has good convergence rate than other algorithms. Distance metric is used...

Data clustering is one of the central concepts in the field of unsupervised data analysis and machine learning, but it is also a surprisingly controversial issue, and the very meaning of the concept “clustering” may vary a great deal between different scientific disciplines (see, e.g., [1] and the references therein). However, a common goal in all cases is that the objective is to find a struct...

Mixture models have received a great deal of attention in statistics due to the wide range applications found recent years. This paper discusses finite mixture model Birnbaum–Saunders distributions with G components, which is an important supplement that developed by Balakrishnan et al. (J Stat Plann Infer 141:2175–2190, 2011) who considered two components. Our proposal enables modeling proper ...