Exact array dataaow analysis can be achieved in the general case if the only control structures are the sequence, the do-loop and restricted ifs, and if loop counter bounds and array subscripts are aane expressions of surrounding loop counters and possibly some integer constants. In this paper, we begin the study of dataaow analysis of dynamic control programs, where arbitrary ifs and whiles ar...

Data obtained in association with many real-life random experiments from different fields cannot be perfectly/exactly quantified.hspace{.1cm}Often the underlying imprecision can be suitably described in terms of fuzzy numbers/\values. For these random experiments, the scale of fuzzy numbers/values enables to capture more variability and subjectivity than that of categorical data, and more accur...

A line of research is presented aimed at specifying both logical and navigational aspects of semi-structured data sources such as Web sites through the unifying notion of schema. Gracefully supporting schemata that are huge or subject to change, the WG-Log language allows for a uniform representation of queries and views, the latter expressing customized access structures to site information. A...

Clustering algorithm is very important for data mining. Fuzzy c-means clustering algorithm is one of the earliest goal-function clustering algorithms, which has achieved much attention. This paper analyzes the lack of fuzzy C-means (FCM) algorithm and genetic clustering algorithm. Propose a hybrid clustering algorithm based on immune single genetic and fuzzy C-means. This algorithm uses the fuz...

In the last decades, a number of robust fuzzy clustering algorithms have been proposed to partition data sets affected by noise and outliers. Robust fuzzy C-means (robust-FCM) is certainly one of the most known among these algorithms. In robust-FCM, noise is modeled as a separate cluster and is characterized by a prototype that has a constant distance δ from all data points. Distance δ determin...

This paper presents fuzzy clustering algorithms for mixed features of symbolic and fuzzy data. El-Sonbaty and Ismail proposed fuzzy c-means (FCM) clustering for symbolic data and Hathaway et al. proposed FCM for fuzzy data. In this paper we give a modi3ed dissimilarity measure for symbolic and fuzzy data and then give FCM clustering algorithms for these mixed data types. Numerical examples and ...

Data Mining is a new filed in data processing research. Support Vector Machine (SVM) is one of the new methods using in data mining, which has gained great applicable success. However, there are still plenty of limitations in SVM. For example, SVM won’t work if its training set contains uncertain information. In order to solve the problem presented above, this paper discusses the constraining p...

1. UNCERTAINTY: ONE MUST FIGHT, BUT ONE CAN’T WIN 9 1.1. To recognize a situation 9 1.2 Predicting tomorrow 11 1.3 An expert and his cognitive activity 12 1.4 Statistics and quasi-statistics 13 2. FUZZY SETS COME TO THE AID 14 2.1 The Carrier 14 2.2 Fuzzy set 15 2.3 The Function of Belonging 15 2.4 The Linguistic Variable 15 2.5 Operations on fuzzy sub-sets 16 2.6 Fuzzy numbers and operations o...

The fact of building an accurate classification and prediction system remains one of the most significant challenges in knowledge discovery and data mining. In this paper, a Knowledge Discovery (KD) framework is proposed; based on the integrated fuzzy approach, more specifically Fuzzy C-Means (FCM) and the new Multiple Support Classification Association Rules (MSCAR) algorithm. MSCAR is conside...