In this paper we propose a fully unsupervised approach for product aspect discovery in on-line consumer reviews. We apply a twostep hierarchical clustering process in which we first cluster words representing aspects based on the semantic similarity of their contexts and then on the similarity of the hypernyms of the cluster members. Our approach also includes a method for assigning class label...

In this paper, a clustering-based method is proposed for automatically constructing a multi-input TakagiSugeno (TS) fuzzy model where only the input-output data of the identified system are available. The TS fuzzy model is automatically generated by the process of structure identification and parameter identification. In the structure identification step, a clustering method is proposed to prov...

— Clustering ensemble is one of the most recent advances in unsupervised learning. It aims to combine the clustering results obtained using different algorithms or from different runs of the same clustering algorithm for the same data set, this is accomplished using on a consensus function, the efficiency and accuracy of this method has been proven in many works in literature. In the first part...

This paper describes a probabilistic framework for graph-clustering. We commence from a set of pairwise distances between graph-structures. From this set of distances, we use a mixture model to characterize the pairwise affinity of the different graphs. We present an EM-like algorithm for clustering the graphs by iteratively updating the elements of the affinity matrix. In the M-step we applyin...

vi Chapter 1 Literature Review and Requirements Analysis 1 1.1 Functional analysis of microarray data 1 1.2 Gene Ontology-based functional analysis 1 1.3 Literature-based functional analysis 4 1.3.1 Assuming similar expressions imply same functional pathway 5 1.3.2 Not assuming similar expressions imply same functional pathway 7 1.4 Hybrid systems 10 1.5 Requirements analysis 11 Chapter 2 Strat...