In this paper, we study different discrete data clustering methods, which use the Model-Based Clustering (MBC) framework with the Multinomial distribution. Our study comprises several relevant issues, such as initialization, model estimation and model selection. Additionally, we propose a novel MBC method by efficiently combining the partitional and hierarchical clustering techniques. We conduc...

The Multiple Prototype Fuzzy Clustering Model (FCMP), introduced by Nascimento, Mirkin and Moura-Pires (1999), proposes a framework for partitional fuzzy clustering which suggests a model of how the data are generated from a cluster structure to be identi...ed. In the model, it is assumed that the membership of each entity to a cluster expresses a part of the cluster prototype re‡ected in the e...

DIVCLUS-T is a descendant hierarchical clustering methods based on the same monothetic approach than segmentation but from an unsupervised point of view. The dendrogram of the hierarchy is easy to interpret and can be read as decision tree. We present DIVCLUS-T on a small numerical and a small categorical example. DIVCLUS-T is then compared with two polythetic clustering methods: the Ward ascen...

The tunicate swarm algorithm (TSA) is a newly proposed population-based optimizer for solving global optimization problems. TSA uses best solution in the population order improve intensification and diversification of tunicates. Thus, possibility finding better position search agents has increased. aim clustering algorithms to distributed data instances into some groups according similar dissim...

Partitional clustering methods such as C-Means classify all samples into clusters. Even a noise sample that is distant from any cluster is assigned to one of the clusters. Noise samples included in clusters bias the clustering result and tend to produce meaningless clusters. Our clustering method repeats to extract mutually close samples as a cluster and leave isolated noises unclustered. Thus,...

This research applies a Neuro-fuzzy method for clustering greenhouse gases produced by student activities. The partitional clustering algorithm is combined with Neuro-fuzzy. A standard dataset including iris and breast cancer is used to test the ability of the clustering algorithm. All activities who live in university dormitories are used to calculate the coefficient greenhouse gas emissions. ...

2 Clustering Techniques: A Brief Survey 4 2.1 Partitional Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Hierarchical Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.3 Discriminative vs. Generative Models . . . . . . . . . . . . . . . . . 12 2.4 Assessment of Results . . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.4.1 Internal (model-based, unsup...

In this paper we investigate clustering in the weighted setting, in which every data point is assigned a real valued weight. We conduct a theoretical analysis on the influence of weighted data on standard clustering algorithms in each of the partitional and hierarchical settings, characterising the precise conditions under which such algorithms react to weights, and classifying clustering metho...

Anomaly detection is concerned with identification of abnormal patterns of behavior of a system. Traditional supervised machine learning methods of classification rely on training data in the form of labeled data instances representative of each class (e.g. normal vs anomalous data). Clustering methods, on the other hand, do not require a priori knowledge of how anomalies are represented in the...