Classification task involves inducing a predictive model using a set of labeled samples. The more the labeled samples are, the better the model is. When one has only a few samples, the obtained model tends to offer poor result. Even when labeled samples are difficult to get, a lot of unlabeled samples are generally available on which unsupervised learning can be used. In this paper, a way to co...

This report describes classification methods that recognize the genres of music using both supervised and unsupervised learning techniques. The five genres, classical(C), EDM(E), hip-hop(H), jazz(J) and rock(R), were examined and classified. As a feature selection method, discrete Fourier transform (DFT) converted the raw wave signals of each song into the signal amplitude ordered by their freq...

State of the Art of Automatic Keyphrase Extraction Methods This article presents the state of the art of the automatic keyphrase extraction methods. The aim of the automatic keyphrase extraction task is to extract the most representative terms of a document. Automatic keyphrase extraction methods can be divided into two categories : supervised methods and unsupervised methods. For supervised me...

Nowadays, the massive increment in applications running on a computer and excessive in network services forces to take convenient security policies into an account. Many methods of intrusion detection proposed to provide security in a computer system and network using data mining methods. These methods comprise of the outlier, unsupervised and supervised methods. As we know, each data mining me...

In this paper, a cluster validity concept from an unsupervised to a supervised manner is presented. Most cluster validity criterions were established in an unsupervised manner, although many clustering methods performed in supervised and semi-supervised environments that used context information and performance results of the model. Context-based clustering methods can divide the input spaces u...

Inference of gene regulatory network from expression data is a challenging task. Many methods have been developed to this purpose but a comprehensive evaluation that covers unsupervised, semi-supervised and supervised methods, and provides guidelines for their practical application, is lacking. We performed an extensive evaluation of inference methods on simulated and experimental expression da...

Cancer classification is one major application of microarray data analysis. Due to the ultra high dimensionality nature of microarray data, data dimension reduction has drawn special attention for such type of data analysis. The currently available data dimension reduction methods are either supervised, where data need to be labeled, or computational complex. In this paper, we proposed to use a...

Computer Aided Diagnosis (CAD) tools are often needed for fast and accurate detection, characterization, and risk assessment of different tumors from radiology images. Any improvement in robust and accurate image-based tumor characterization can assist in determining non-invasive cancer stage, prognosis, and personalized treatment planning as a part of precision medicine. In this study, we prop...