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Data mining concerns theories, methodologies, and in particular, computer systems for knowledge extraction or mining from large amounts of data. Association rule mining is a general purpose rule discovery scheme. It has been widely used for discovering rules in medical applications. The diagnosis of diseases is a significant and tedious task in medicine. The detection of heart disease from various...
This paper investigates a method for instance selection in the context of supervised classification adapted to large databases. Based on the scale up concept, the method reduces the time required to perform the selection procedure by enabling the application of known condensation instance techniques to only small data sets instead of the whole set. The novelty of our approach relies in the way of...
We present in this paper a new method for the design of evolving neuro-fuzzy classifiers. The presented approach is based on a first-order Takagi-Sugeno neuro-fuzzy model. We propose a modification on the premise structure in this model and we provide the necessary learning formulas, with no problem-dependent parameters. We demonstrate by the experimental results the positive effect of this modification...
In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system, that can be integrated into an application to provide personalization capabilities...
Information Retrieval is a well established interdisciplinary topic in which machine learning, computational linguistic, computer programming and data mining merge together. SLAIR stands for Sea Lab Advanced Information Retrieval and is an efficient software architecture that embeds these issues in a unique framework. SLAIR is expandable both from the data format and algorithm point of view. A pluggable...
Clustering, an supervised learning process is a challenging problem. Clustering result quality improves the overall structure. In this article, we propose an incremental stream of hierarchical clustering and improve the efficiency, reduce time consumption and accuracy of text categorization algorithm by forming an exact sub clustering. In this paper we propose a new method called multilevel clustering...
This paper studies the web wrapper generation for web pages of forum, blog and news web sites. While more and more web pages are dynamically generated using a common template populated with data from databases. This paper proposes a novel method that uses tree alignment and transfer learning method to generate the wrapper from this kind of web pages. We present a new tree alignment algorithm to find...
Shape similarity and shape retrieval are very important topics in computer vision. The recent progress in this domain has been mostly driven by designing smart shape descriptors for providing better similarity measure between pairs of shapes. In this paper, we provide a new perspective to this problem by considering the existing shapes as a group, and study their similarity measures to the query shape...
Segmenting large or multiple images is time and memory consuming. These issues have been addressed in the past by implementing parallel versions of popular algorithms such as Graph Cuts and Mean Shift. Here, we propose to use an incremental Gaussian Mixture Model (GMM) learning algorithm for parallel image segmentation. We show that our approach allows us to reduce the memory requirements dramatically...
In this paper we propose a new clustering algorithm which combines the FCM clustering algorithm with the supervised learning normal mixture model; we call the algorithm as the FCM-SLNMM clustering algorithm. The FCM-SLNMM clustering algorithm consists of two steps. The FCM algorithm was applied in the first step. In the second step the supervised learning normal mixture model was applied and the clustering...
The BITQAP question answering platform is ontology-based semantic question answering system. In ldquoComputer Organization and Architecturerdquo course, we apply the platform as CAI tools to answer questions. In the paper, we proposed methods to extract the concepts and relations from course corpus. Term extraction is the first step of ontology learning. The Chinese domain terms have three features:...
Many enterprises incorporate information gathered from a variety of data sources into an integrated input for some learning task. For example, aiming towards the design of an automated diagnostic tool for some diseases, one may wish to integrate data gathered from many different hospitals. Analyzing and mining these distributed heterogeneous data sources require distributed machine learning and data...
Mining association rules plays an essential role in data mining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Although we can transform quantitative attributes into intervals and applying Boolean algorithms to the intervals. But this approach is not effective and is difficult to scale up for high-dimensional...
In this paper a complete OCR methodology for recognizing historical documents, either printed or handwritten without any knowledge of the font, is presented. This methodology consists of three steps: The first two steps refer to creating a database for training using a set of documents, while the third one refers to recognition of new document images. First, a pre-processing step that includes image...
With the rapid developing of the network information, it seems to be quite important to provide a more reasonable text classification algorithm for learners. In this paper,we adopt a sensitivity method to modify the characteristic weight in the distance formula and put up with a cutting method of training sample database based on CURE algorithm and Tabu algorithm; then adopt CURE cluster algorithm...
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