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Document clustering is a popular topic in data mining and information retrieval. Most models and methods for this problem are based on computing the similarity between pair documents modeled in a space of all terms, or a new feature space obtained by applying a topic modeling technique for a given corpus. In this paper, we regard these two ideas as clustering on term feature and on semantic feature,...
This paper focuses on the clustering segmentation of 3D color point cloud. We extend the mean shift algorithm to the 3D xyz space, and what's more, we also consider the rgb color information, so the 6 dimensional data is adopted in the algorithm. The cluster center converges to the joint position of the local maximum density and the minimum gradient change of color, so our clustering segmentation...
Shape-specific points are special data points invariant to translation, scaling, and rotation. The radius weighted mean (RWM) and the system center are two examples of shape-specific points. These points feature in contour registration, color quantization, and the detection of rotationally symmetric shape orientations. This study uses shape-specific points to cluster nonlinearly separable data into...
Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. If rule-based systems and supervised methods usually provide the best detection and prevention, labelled training datasets are often non-existent and such solutions lack reactivity when facing adaptive fraudsters. Many generic...
Image segmentation, an essential process of pixel clustering partitions raw image into non-overlapping regions. This paper surveys the image segmentation techniques based on the clustering. From the survey it is clear that clustering plays an important role in image segmentation.
Brain tumor occurs when abnormal growth of tissues or cells in the brain is called brain tumor. Presently using medical imaging techniques are Magnetic Resonance Imaging (MRI), Computerised tomography (CT) and Micro wave, which cannot detect below 3mm size but it can be detected by Near Infrared Imaging Technology, fuzzy clustering, fuzzy LMS, Seed Growing, Electromagnetic Optimization Techniques,...
The clustering coefficient and the transitivity ratio are concepts often used in network analysis, which creates a need for fast practical algorithms for counting triangles in large graphs. Previous research in this area focused on sequential algorithms, MapReduce parallelization, and fast approximations. In this paper we propose a parallel triangle counting algorithm for CUDA GPU. We describe the...
Future automotive radars will be able to achieve much higher range and angular resolution compared to currently used radar sensors. This enables functionalities like vehicle contour estimation to be used in advanced driver assistance systems, thus heavily increasing their performance. In this paper, the application of an adaptive algorithm on basis of k-nearest-neighbours examination for clustering...
Performance of the support vector machine strongly depends on parameters settings. One of the most common algorithms for parameter tuning is grid search, combined with cross validation. This algorithm is often time consuming and inaccurate. In this paper we propose the use of stochastic metaheuristic algorithm, firefly algorithm, for effective support vector machine parameter tuning. The experimental...
Image segmentation has key influence in numerous medical imaging uses. In this paper, we present a new algorithm for spatial fuzzy segmentation using modified particle swarm optimization of medical & multimedia data. The algorithm is realized by modifying the scaling parameters in the conventional fuzzy C-means (FCM) algorithm using Modified Particle Swarm Optimization (MPSO). Spatial coordinates...
In this paper, we propose two novel active learning algorithms: 1) k-mode for classifying the certain and uncertain dataset in a whole dataset, 2) Priority R-Tree clustering the certain and uncertain data for each domain. They handle both supervised and unsupervised dataset. These techniques improve the robustness and accuracy of the clustering outcome to a great extent. By minimizing the expected...
We propose a novel univariate time series decomposition algorithm to partition temporal sequences into homogeneous segments. Unlike most existing temporal segmentation approaches, which generally build statistical models of temporal observations and then detect change points using inference or hypothesis testing techniques, our algorithm requires no domain knowledge, is insensitive to the choice of...
Focusing on the issue of analog circuit performance online evaluation, the arithmetic speed and the evaluation reliability should be considered simultaneously. A novel online faults diagnosis strategy based on modified kernel fuzzy C-means (β-MKFCM) is proposed based unsupervised learning algorithms of analog circuit faults diagnosis for the known faults and unknown faults online. More specially,...
Detecting causal relationships between time series data has been widely studied in many areas, including biology, neuroscience, economics, and climatology. One of the most popular causality inference methods is Granger causality approach that is a linear regression based model for determining whether one time series is useful in forecasting another; however, this approach cannot detect nonlinear relations...
This paper presents a survey of Hybrid fuzzy c-means (FCM) clustering algorithms, The algorithmic steps, parameters involved in the algorithm & the experimental results on various datasets of several hybrid clustering methods are discussed in this paper. Hybrid FCM clustering techniques are obtained by modifying the FCM either by incorporating hesitation degree of Intuionistic approach or by replacing...
Spatio - temporal methods is the process of innovations and finding the patterns from the knowledge representations through outliers. This kind of data representing the (i) the states of an object (ii) position or event in space at a particular period of time. It refers to the Objects whose attribute values are entirely different from its neighbourhood. Always their locations are different even the...
This research is concerned with the table based KNN as the approach to the keyword extraction task. The keyword extraction task is viewed as an instance of word classification, and it is discovered that encoding words into tables improved the word categorization performance. In this research, words are encoded into tables and the correspondingly modified version of KNN is applied to the keyword extraction...
In this research, we propose the table based KNN as the approach to the text categorization. In previous works, we discovered that encoding texts into tables improved the performance in the text categorization, so in this research, become to consider the possibility of encoding words into tables as well as texts. In this research, we encode words into tables where entries are texts and their weights,...
We concern this research with the table based KNN as the approach to the index optimization task. It may be interpreted into an instance of word classification, and the encoding scheme where words are encoded into tables improved the task word classification. In this research, words are encoded into tables and apply the table based KNN to the index optimization task. From this research, we expect...
This research proposes the table based AHC algorithm as the approach to the word clustering task. The results from encoding texts into tables were successful in the previous works on the text categorization and the text clustering, and if oppositely to the case of the text encoding, texts are assumed to be elements of each word, it becomes to be possible to encode words into tables. In this research,...
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