Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning...

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping, or clustering, objects according to measured or perceived intrinsic characte...

Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X x Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this assumption has been explored and the importance ...

1. Spatial clustering of DNA sequences, output can be directly integrated with Google Maps using OEŞŞŚĤ;;ššš:ŝŚŋŞoeŋŖŏŚoeŎŏŗoeřŖřőţ:ŘŏŞ;. 2. Trained clustering (i.e. semi-supervised classification) of DNA sequence data. 3. Tandem command line program hierBAPS for clustering DNA sequence data in a hierarchical manner and for visualization of the results up to whole genome scale.

Ensemble methods are known to increase the performance of learning algorithms, both on supervised and unsupervised learning. Boosting algorithms are quite successful in supervised ensemble methods. These algorithms build incrementally an ensemble of classifiers by focusing on objects previously misclassified while training the current classifier. In this paper we propose an extension to the Evi...

We explore unsupervised and supervised whole-document approaches to English NEL with naı̈ve and context clustering. Our best system uses unsupervised entity linking and naı̈ve clustering and scores 66.5% B+ F1 score. Our KB clustering score is competitive with the top systems at 65.6%.

Traditional clustering algorithms usually rely on a pre-defined similarity measure between unlabelled data to attempt to identify natural classes of items. When compared to what a human expert would provide on the same data, the results obtained may be disappointing if the similarity measure employed by the system is too different from the one a human would use. To obtain clusters fitting user ...