The wealth of interaction information provided in biomedical articles motivated the implementation of text mining approaches to automatically extract biomedical relations. This paper presents an unsupervised method based on pattern clustering and sentence parsing to deal with biomedical relation extraction. Pattern clustering algorithm is based on Polynomial Kernel method, which identifies inte...

Purpose: Completely labeled datasets of pathology slides are often difficult and time consuming to obtain. Semi-supervised learning methods are able to learn reliable models from small number of labeled instances and large quantities of unlabeled data. In this paper, we explored the potential of clustering analysis for semi-supervised support vector machine (SVM) classifier. Method: A clusterin...

Semi-supervised learning algorithms commonly incorporate the available background knowledge such that an expression of the derived model’s quality is improved. Depending on the specific context quality can take several forms and can be related to the generalization performance or to a simple clustering coherence measure. Recently, a novel perspective of semi-supervised learning has been put for...

This paper describes a rather novel method for the supervised training of regression systems that can be an alternative to feedforward Artificial Neural Networks (ANNs) trained with the BackPropagation algorithm. The proposed methodology is a hybrid structure based on supervised clustering with genetic algorithms and local learning. Supervised Scaled Regression Clustering with Genetic Algorithm...

In this paper, we propose to fuse both clustering and supervised classification approach in order to outperform the results of a classification algorithm. Indeed the results of the learning in supervised classification depend on the method and on the parameters chosen. Moreover the learning process is particularly difficult which few learning data and/or imprecise learning data. Hence, we defin...

Semi-supervised clustering on information networks combines both the labeled and unlabeled data sets with an aim to improve the clustering performance. However, the existing semi-supervised clustering methods are all designed for homogeneous networks and do not deal with heterogeneous ones. In this work, we propose a semi-supervised clustering approach to analyze heterogeneous information netwo...

Traditionally, supervised machine learning methods are the first choice for tasks involving classification of data. This study provides a non-conventional hybrid alternative technique (pEAC) that blends the Possibilistic Fuzzy CMeans (PFCM) as base cluster generating algorithm into the ‘standard’ Evidence Accumulation Clustering (EAC) clustering method. The PFCM coalesces the separate propertie...

We apply an unsupervised machine learning approach for Internet traffic identification and compare the results with that of a previously applied supervised machine learning approach. Our unsupervised approach uses an Expectation Maximization (EM) based clustering algorithm and the supervised approach uses the Naı̈ve Bayes classifier. We find the unsupervised clustering technique has an accuracy ...

Semi Supervised methods use a small amount of auxiliary information as a guide in the learning process in presence of unlabeled data. When using a clustering algorithm, the auxiliary information has the form of side information, that is a list of co-clustered points. Recent literature shows better performance of these methods with respect to totally unsupervised ones even with a small amount of...