Approaches to object localization based on codebooks do not exploit the dependencies between appearance and geometric information present in training data. This work addresses the problem of computing a codebook tailored to the task of localization by applying regularization based on geometric information. We present a novel method, the Regularized Combined Partitional-Agglomerative clustering,...

Clustering of high-dimensional data can be problematic, because the usual notions of distance or similarity break down for data in high dimensions. More specifically, it can be shown that, as the number of dimensions increases, the distance to the nearest point approaches the distance to the farthest one. Two approaches are common for dealing with this problem. The idea behind the first approac...

Clustering of sequential or temporal data is more challenging than traditional clustering as dynamic observations should be processed rather than static measures. This paper proposes a Hidden Markov Model (HMM)-based technique suitable for clustering of data sequences. The main aspect of the work is the use of a probabilistic model-based approach using HMM to derive new proximity distances, in ...

Multispectral images such as multispectral chemical images or multispectral satellite images provide detailed data with information in both the spatial and spectral domains. Many segmentation methods for multispectral images are based on a per-pixel classification, which uses only spectral information and ignores spatial information. A clustering algorithm based on both spectral and spatial inf...

Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. The bag of words representation used for these clustering methods is often unsatisfactory as it ignores relationships between important terms that do not co-occur literally. In order to deal with the pro...

Handcrafting semantic classes is a difficult and time-consuming job, and depends on human interpretation. Possible machine learning techniques would be much faster, and do not rely on interpretation, because they stick to the data. The goal of this research is to present some machine learning techniques that make it possible to achieve an automatic clustering of Dutch words. More particularly, ...

Data with categorical attributes are ubiquitous in the real world. However, existing partitional clustering algorithms for categorical data are prone to fall into local optima. To address this issue, in this paper we propose a novel clustering algorithm, ABC-K-Modes (Artificial Bee Colony clustering based on K-Modes), based on the traditional k-modes clustering algorithm and the artificial bee ...

The determination of the number of groups in a dataset, their composition and the most relevant measurements to be considered in clustering the data, is a high-demanding task, especially when the a priori information on the dataset is limited. Some different genetic approaches are proposed as tools for automatic data clustering and features selection. They differ in the adopted codification of ...

Data in various systems, such as those in finance, healthcare, and business, are stored as time series. As such, interest in time series mining in these areas has surged. Clustering of data is performed as a pre-processing or exploratory approach in many data mining tasks. Time series data sets are often very large, thus, data cannot fit in the main memory for clustering. In this case, dimensio...