Background and Objective: Colorectal cancer (CRC) is one of the most prevalent malignancies in the world. The early detection of CRC is not only a simple process, but it is also the key to its treatment. Given that data mining algorithms could be potentially useful in cancer prognosis, diagnosis, and treatment, the main focus of this study is to measure the performance of some data mining class...

Fuzzy rough set method provides an effective approach to data mining and knowledge discovery from hybrid data including categorical values and numerical values. However, its time-consumption is very intolerable to analyze data sets with large scale and high dimensionality. Many heuristic fuzzy-rough feature selection algorithms have been developed however, quite often, these methods are still c...

Nowadays companies measure suppliers on the basis of a variety of factors and criteria that affect the supplier's selection issue. This paper intended to identify the key effective criteria for selection of green suppliers through an efficient algorithm callediterative process mining or i-PM. Green data were collected first by reviewing the previous studies to identify various environmental cri...

In this paper, we are interested in discovering genetic and environmental factors that are involved in multifactorial diseases. Experiments have been achieved by the Biological Institute of Lille and many data has been generated. To exploit these data, data mining tools are required and we propose a two-phase optimisation approach using a speci®c genetic algorithm. During the ®rst step, we sele...

Feature selection determines relevant features in the data. It is often applied in pattern classiica-tion. A special constraint for feature selection nowadays is that the size of a database is normally very large. An eeective method is needed to accommodate the practical demands. A scalable probabilistic algorithm is presented here as an alternative to the exhaustive and heuristics approaches. ...

To choose the best features in data mining issues, the Relief Feature Selection Algorithm is proposed to implement the feature selection in this paper. Firstly, the data of Ionosphere from the UCI (University of California Irvine) database is used to do a simulation experiment; secondly, the proposed method is employed to do feature selection for voice signal. In this case study, the study star...

Summarization, an extremely important technique in Data Mining is an automatic learning technique aimed to extract the most valuable information from a large size document or the articles. The goal is to create the summary of the document, but substantially different from each other. Text Document summarization refers to the summarization of text documents based upon their content. The proposed...

Feature Selection plays the very important role in Intrusion Detection System. One of the major challenge these days is dealing with large amount of data extracted from the network that needs to be analyzed. Feature Selection helps in selecting the minimum number of features from the number of features that need more computation time, large space, etc. This paper, analyzed different feature sel...

Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of...

business related decisions become more perplexing as societies advance. environmental ambiguities which stem from new business models intensify the above mentioned complexity so far as many of the affecting variables and their interrelationships are non linear and complicated. in this circumstances data analysis and knowledge extraction is not practical via traditional methods. this research co...