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Defining a boundary between inliers and outliers is a major challenge in unsupervised outlier detection. In the absence of labeled data, the true outliers set cannot be evaluated. This lays the burden on both the choice of an efficient outlier detection criterion, and parameter selection. While numerous unsupervised outlier detection criteria, with different parameters, have been proposed, an unsupervised...
This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing...
In this paper we propose a new approach based on Symbolic Aggregate approximation (SAX), called improved iSAX to recognize efficient and accurate discovery of the important patterns, essential for time series data. The original SAX approach allows a very high-quality dimensionality reduction and distance measures to be defined on the symbolic approach and it is based on PAA (Piecewise Aggregate Approximation)...
Research in systems biology integrates experimental, theoretical, and modeling techniques to study and understand biological processes such as gene regulation. The genomic sequences for human and other model organisms such as yeast and bacteria are already established. The next major step is to discover functional roles of genes whose functions are not yet discovered and to investigate how genes interact...
In the recent years human brain segmentation in three-dimensional magnetic resonance imaging (MRI) has gained a lot of importance in the field of biomedical image processing since it is the main stage for the automatic brain disease diagnosis. In this paper, we propose an image segmentation scheme to segment 3D brain tumor from MRI images through the clustering process. The clustering is achieved...
This paper aims to assess the effectiveness of three different clustering algorithms, used to detect breast cancer recurrent events. The performance of a classical k-means algorithm is compared with a much more sophisticated Self-Organizing Map (SOM-Kohonen network) and a cluster network, closely related to both k-means and SOM. The three clustering algorithms have been applied on a concrete breast...
Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In...
Visualization techniques provide attractive tools to explore and analyze huge and high dimensional gene expression sets. Several visualization techniques have been developed that enabled users to visually analyze high dimensional data. However, these techniques should be integrated with efficient exploration techniques, as efficient clustering, outlier analysis, ensembles and cluster validation to...
This paper presents a semi-automatic system for home video annotation that searches into the video contents and retrieves video shots for a specific person. The proposed system is composed of four phases; 1) shot detection phase that detects shots boundaries and divides the original video into shots, 2) face detection and recognition phase that detects faces in video shots based on Haar-like features...
The advantages of soft c-means over its hard and fuzzy versions render it more attractive to use in a wide variety of applications. Its main merit lies in its relatively higher convergence speed, which is more obvious in the presence of huge high dimensional data. This work presents a new approach to accelerate the convergence of the original soft c-means. It is mainly based on an iterative optimization...
This paper introduces a relational fuzzy c-means clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions...
Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related...
Feature selection is a very important preprocessing step in data classification. By applying it we are able to reduce the dimensionality of the problem by removing redundant or irrelevant data. High dimensional data sets are becoming usual nowadays specially in bio-informatics, biology, signal processing or text classification, increasing the need for efficient feature selection methods. In this paper...
Advances in DNA microarray technology has motivated the research community to introduce sophisticated techniques for analyzing the resulted large-scale datasets. Biclustering techniques have been widely adapted for analyzing microarray gene expression data due to its ability to extract local patterns with a subset of genes that are similarly expressed over a subset of samples. Mostly, biclustering...
Support Vector Machines (SVMs) ensembles have been widely used to improve classification accuracy in complicated pattern recognition tasks. In this work we propose to apply an ensemble of SVMs coupled with feature-subset selection methods to aleviate the curse of dimensionality associated with expression-based classification of DNA microarray data. We compare the single SVM classifier to SVM ensembles...
Relevance feedback (RFB) involves requesting some user judgments for an initial set of search results and then using these judgments to improve search results. Typical queries may have multiple possible interpretations or facets, only one of which is relevant to a user's need, but top search results may be dominated by one interpretation or facet. Thus, if the user is only given the top results to...
In this paper we present an automatic authority control system for raw noisy web data based on Data Mining. We use a hierarchical clustering approach with a special distance measure combination of three parameters: author name similarity, token similarity and co-authors similarity, each one defined in a specific way. A preliminary experimental study has been performed with real data obtained from...
To date, various fields of applications have utilized spatio-temporal databases not only to store data, but to support decision making. For example, in traffic accident analysis; it is required to have knowledge on the pattern of accidents resulting in death. Thus, in such analysis, clustering technique is desired to implement pattern extraction. This paper presents clustering of spatio-temporal database...
Unequal Area Facility Layout Problem (UA-FLP) has been addressed by several methods. However, UA-FLP has only been solved regarding quantitative criteria. Our approach includes subjective features to UA-FLP, which are difficult to take into account with a classical heuristic optimization. For that, an Interactive Genetic Algorithm (IGA) is proposed that allows an interaction between the algorithm...
This paper presents a model of a supervised machine learning approach for classification of a dataset. The model extracts a set of patterns common in a single class from the training dataset according to the rules of the pattern-based subspace clustering technique. These extracted patterns are used to classify the objects of that class in the testing dataset. The user-defined threshold dependence...
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