ABSTRACT: Based on clustering algorithm Affinity Propagation (AP) I present this paper a semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in my approach: 1) a similarity metric that captures the structural information of texts, and 2) seed construction method to improve the semisupervised clustering process. To study the perform...

ABSTRACT Clustering algorithms have become increasingly important in handling and analyzing data. Considerable work has been done in devising e ective but increasingly speci c clustering algorithms. In contrast, we have developed a generalized framework that accommodates diverse clustering algorithms in a systematic way. This framework views clustering as a general process of iterative optimiza...

Semi-supervised clustering uses a small amount of supervised data to aid unsupervised learning. One typical approach specifies a limited number of must-link and cannotlink constraints between pairs of examples. This paper presents a pairwise constrained clustering framework and a new method for actively selecting informative pairwise constraints to get improved clustering performance. The clust...

Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some “weak” form of side information about the domain or data sets can be often availabl...

Feature-based hot spots are localized regions where the attributes of objects attain high values. There is considerable interest in automatic identification of feature-based hot spots. This paper approaches the problem of finding feature-based hot spots from a data mining perspective, and describes a method that relies on supervised clustering to produce a list of hot spot regions. Supervised c...

Fuzzy decision tree induction algorithms require the fuzzy quantization of the input variables. This paper demonstrates that supervised fuzzy clustering combined with similarity-based rule-simplification algorithms is an effective tool to obtain the fuzzy quantization of the input variables, so the synergistic combination of supervised fuzzy clustering and fuzzy decision tree induction can be e...

Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel methods for semi-supervised clustering not only face the problem of manually tuning the kernel parameters due to the fact that no sufficient supervision is provided, but also lack a measure that achieves better effectiven...

We introduce a principled method for the signed clustering problem, where goal is to partition weighted undirected graph whose edge weights take both positive and negative values, such that edges within same cluster are mostly positive, while spanning across clusters negative. Our relies on graph-based diffuse interface model formulation utilizing Ginzburg-Landau functional, based an adaptation...

Semi-Supervised clustering algorithms often utilize a seeds set consisting of a small amount of labeled data to initialize cluster centroids, hence improve the clustering performance over whole data set. Both the scale and quality of seeds set directly restrict the performance of semi-supervised clustering algorithm. In this paper, a new algorithm named DE-Tri-training semi-supervised K-means i...

We propose a new methodology for clustering data comprising multiple domains or parts, in such a way that the separate domains mutually supervise each other within a semi-supervised learning framework. Unlike existing uses of semi-supervised learning, our methodology does not assume the presence of labels from part of the data, but rather, each of the different domains of the data separately un...