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.

In this paper, we propose a semi-supervised clustering method, CECIB, that models data with a set of Gaussian distributions and that retrieves clusters based on a partial labeling provided by the user (partition-level side information). By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting g...

In this paper, Design and Implementation of Binary Neural Network Learning with Fuzzy Clustering (DIBNNFC), is proposed to classify semisupervised data, it is based on the concept of binary neural network and geometrical expansion. Parameters are updated according to the geometrical location of the training samples in the input space, and each sample in the training set is learned only once. It...

The minimum sum-of-squares clustering (MSSC), or k-means type clustering, is traditionally considered an unsupervised learning task. In recent years, the use of background knowledge to improve cluster quality and promote interpretability process has become a hot research topic at intersection mathematical optimization machine research. problem taking advantage information in data called semi-su...

Although many studies have been conducted to improve the clustering efficiency, most of the state-of-art schemes suffer from the lack of robustness and stability. This paper is aimed at proposing an efficient approach to elicit prior knowledge in terms of must-link and cannot-link from the estimated distribution of raw data in order to convert a blind clustering problem into a semi-supervised o...