Data mining is the central step in a process called knowledge discovery in databases, namely the step in which modeling techniques are applied. Several research areas like statistics, artificial intelligence, machine learning, and soft computing have contributed to its arsenal of methods. In this paper, however, we focus on fuzzy methods for rule learning, information fusion, and dependency ana...

Data mining is the process of extracting hidden patterns from huge data. Among the various clustering algorithms, k-means is the one of most widely used clustering technique in data mining. The performance of k-means clustering depends on the initial clusters and might converge to local optimum. K-means does not guarantee the unique clustering because it generates different results with randoml...

In 1998, Bonissone (1998) perceived soft computing to be a recently coined term, describing the symbiotic use of many emerging computing disciplines, amongst these Fuzzy Logic (introduced by Zadeh, 1965). They suggested that fuzzy logic gives us a language, with syntax and local semantics, in which we can translate our qualitative knowledge about the problem to be solved, with its main characte...

With the rapid expansion of computer networks during the past few years, security has become a crucial issue for modern computer systems. A good way to detect illegitimate use is through monitoring unusual user activity. The solution is an Intrusion Detection System (IDS) which is used to identify attacks and to react by generating an alert or blocking the unwanted data. For IDS, use of genetic...

Social media sites are now very popular medium for showing your views and opinions to others with a great amount of various types of information uploaded by the social media users, a social web page can be a collection of pages, audio files, photographs, images, video files and other forms of data in structured or unstructured form. It is also huge, diverse, and dynamic, hence raises the scalab...

Although data mining is relative young technique, it has been used in a wide range of problem domains over the past few decades. In this paper, the authors present a new model to forecast the cultivated land demand adopts the technique of data mining. The new model which is called fuzzy Markov Chain model with weights ameliorate the traditional Time Homogeneous Finite Markov chain model to pred...

This paper presents a three-stage approach to data mining which puts special emphasis on the visualization and interpretability of the results. In the first stage, the input data is represented by a selforganizing map in order to allow visualization and to reduce the amount of data while removing noise, outliers, and missing values. Then this preprocessed information is used to identify and dis...

Web Usage Mining is the application of data mining techniques to learn usage patterns from Web server log file in order to understand and better serve the requirements of web based applications. Web Usage Mining includes three most important steps namely Data Preprocessing, Pattern discovery and Analysis of the discovered patterns. One of the most important tasks in Web usage mining is to find ...

A fuzzy sequential pattern consisting of several fuzzy sets represents a frequently occurring behavior related to time and can be discovered from transaction bases. An example is that large purchase amounts of one product were bought by customers after these consumers had bought small purchase amounts of another product. Recently, Hu et al. (2003) proposed a fuzzy data mining method to discover...

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier....