DBSCAN is arguably the most popular density-based clustering algorithm, and it capable of recovering non-spherical clusters. One its main weaknesses that treats all features equally. In this paper, we propose a algorithm calculating feature weights representing degree relevance each feature, which takes density structure data into account. First, improve introduce new called DBSCANR. DBSCANR re...

We propose and analyze new fast feature weighting algorithms based on different types of feature ranking. Feature weighting may be much faster than feature selection because there is no need to find cut-threshold in the raking. Presented weighting schemes may be combined with several distance based classifiers like SVM, kNN or RBF network (and not only). Results shows that such method can be su...

Text Categorization is the process of automatically assigning predefined categories to free text documents. Feature weighting, which calculates feature (term) values in documents, is one of important preprocessing techniques in text categorization. This paper is a comparative study of feature weighting methods in statistical learning of Thai Document Categorization Framework. Six methods were e...

It is usually true that some structures like title can express the main content of texts, and these structures may have an influence on the effectiveness of text categorization. However, the most common feature weighting algorithms, called term frequency-inverse document frequency (TF-IDF) doesn’t think about the structural information of texts. To solve this problem, a new feature weighting al...

A new method of feature weighting, useful also for feature extraction has been described. It is quite efficient and gives quite accurate results. Weighting algorithm may be used with any kind of learning algorithm. The weighting algorithm with k-nearest neighbors model was used to estimate the best feature base for a given distance measure. Results obtained with this algorithm clearly show its ...

Class imbalance problem in data, plays a critical role in use of machine learning methods for text classification since feature selection methods expect homogeneous distribution as well as machine learning methods. This study investigates two different kinds of feature selection metrics (one-sided and two-sided) as a global component of term weighting schemes (called as tffs) in scenarios where...

This article presents a novel bootstrapping approach for improving the quality of feature vector weighting in distributional word similarity. The method was motivated by attempts to utilize distributional similarity for identifying the concrete semantic relationship of lexical entailment. Our analysis revealed that a major reason for the rather loose semantic similarity obtained by distribution...