Clustering or finding a natural grouping of a data set is essential for knowledge discovery in many applications. This chapter provides an overview on emerging trends within the vital research area of clustering including subspace and projected clustering, correlation clustering, semi-supervised clustering, spectral clustering and parameter-free clustering. To raise the awareness of the reader ...

In this paper, we present a semi-supervised learning algorithm for classification of text documents. A method of labeling unlabeled text documents is presented. The presented method is based on the principle of divide and conquer strategy. It uses recursive K-means algorithm for partitioning both labeled and unlabeled data collection. The K-means algorithm is applied recursively on each partiti...

The abundance of unlabeled data makes semi-supervised learning (SSL) an attractive approach for improving the accuracy of learning systems. However, we are still far from a complete theoretical understanding of the benefits of this learning scenario in terms of sample complexity. In particular, for many natural learning settings it can in fact be shown that SSL does not improve sample complexit...

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn th...

Most datasets in real applications come in from multiple sources. As a result, we often have attributes information about data objects and various pairwise relations (similarity) between data objects. Traditional clustering algorithms use either data attributes only or pairwise similarity only. We propose to combine K-means clustering on data attributes and normalized cut spectral clustering on...

A combinatorial random variable is a discrete random variable defined over a combinatorial set (e.g., a power set of a given set). In this paper we introduce combinatorial Markov random fields (Comrafs), which are Markov random fields where some of the nodes are combinatorial random variables. We argue that Comrafs are powerful models for unsupervised and semi-supervised learning. We put Comraf...

With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data mining fields. Multi-view unsupervised or semi-supervised learning, such as co-training, co-regularization has gained considerable attention. Although recently, multi-view clustering (MVC) methods have been developed rap...

In this paper we present a graph-based semisupervised method for solving regression problem. In our method, we first build an adjacent graph on all labeled and unlabeled data, and then incorporate the graph prior with the standard Gaussian process prior to infer the training model and prediction distribution for semi-supervised Gaussian process regression. Additionally, to further boost the lea...

Associating distinct groups of objects (clusters) with contiguous regions of high probability density (high-density clusters), is a central assumption in statistical and machine learning approaches for the classification of unlabelled data. In unsupervised classification this cluster definition underlies a nonparametric approach known as density clustering. In semi-supervised classification, cl...