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A new technique to perform fuzzification of Density based clusters of 2-dimensional data using regression models has been proposed here. Generally, for fuzzification in partition based clusters, one would compute the center of clusters and then assign memberships for the instances based on their relative distances from centers of all clusters, but the same approach cannot be used for density-based...
In the Era of Information, Extracting useful information out of massive amount of data and process them in less span of time has become crucial part of Data mining. CURE is very useful hierarchical algorithm which has ability to identify cluster of arbitrary shape and able to identify outliers. In this paper we have implemented CURE clustering algorithm over distributed environment using Apache Hadoop...
Data mining has gained much importance in the field of research these days. It makes perfect blend for analyzing data of any fields and provide decision based output. Data generation and storage these days are done at high speed. Non stationary systems play holistic role in providing such data. Availability of such data creates scope of analysis for researchers. Such data which are continuous, unbounded,...
This research investigates the performance of a region-based segmentation, K-Means clustering and Fuzzy C-Means (FCM) for two types of orchid Vanda and Ascocentrum genus. Orchid is the largest family of angiosperms. Among this orchid, Vanda and Ascocentrum are the most famous vandaceous. Some of the orchids have the same color, shape, and appearance. Florist sometimes makes mistakes due to this similarity...
Moving object localization is a popular study in contemporary computer vision, provided the fact that many challenging problems, such as illumination changes, obstacles, object shape transformation, etc, are still to be deeply investigated and properly tackled in moving object localization for the time being. In this study, a new clustering-based strategy is introduced to realize the moving object...
Clustering analysis has been widely used in many areas such as astronomy, bioinformatics, and pattern recognition. In 2014, Rodriguez proposed an algorithm based on the idea that cluster centers are characterized by a higher density than their neighbors and by a relatively large distance from points with higher density. But the density relies on cutoff distance, which might be affected by large statistical...
This paper concerns knowledge discovery from ultra-high frequency financial time series of order book data from the London Stock Exchange Rebuild Order Book database. It focuses on extracting frequent patterns from order book shapes, which characterize particular stock market states. Because order book shapes vary considerably in time, it is necessary to convert the original price-volume representation...
This paper presents a new method of template based shell clustering that allows more flexible free deformation of the cluster prototypes with respect to the template-defined shapes. This is achieved via a soft division of the template into several template parts, each allowed to have its own set of transform parameters. A fuzzification factor, inspired by the one used in the standard fuzzy c-means...
Recently, lung cancer has attracted a great deal of interest due to its commonness and deathly nature. By the development of technology, medical imaging methods play important role in both diagnosis and treatment of this cancer. Lung tumor segmentation in CT images is of high importance in many areas like cancer treatment. One of the common methods for segmenting images, used in this article, is image...
In this paper, a validity index method VDOGK, a variation of the index method VDO, for estimating the optimal number of clusters in datasets with concave-/elongated-shaped clusters is presented. The new index uses Gustafson-Kessel FCM to partition the dataset so that geometric-shape-sensitivity problem of FCM can be reduced. It is based on both dispersion and overlap measures, where the dispersion...
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...
Due to the fast growing of the images data repositories, there is a big challenge to organize these repositories and make them easy to search and mine to get knowledge. Image clustering goal is to link each image in an image database with a class label, so similar images are grouped and have the same class label and which are different from other images in a database. Image clustering organize image...
This paper deals with the superpixel segmentation problem using a powerful global optimization technique: Differential Evolution. The algorithm mimics the process of nature evolution to realize efficient optimization, and it poses no restrictions on the form of objective functions. This way, we develop a novel and comprehensive objective function considering both local and global costs in the segmentation,...
Depth estimation, which is mostly performed by stereo vision, is a remarkable task in vision and scene understanding. In this paper, depth map estimation from a single image is investigated and applied in pedestrian candidate generation. To recover accurate depth map from a single image, a Markov Random Field (MRF) model that incorporates both image depth cues and the relationships between different...
Event-based temporal contrast vision sensors such as the Dynamic Vison Sensor (DVS) have advantages such as high dynamic range, low latency, and low power consumption. Instead of frames, these sensors produce a stream of events that encode discrete amounts of temporal contrast. Surfaces and objects with sufficient spatial contrast trigger events if they are moving relative to the sensor, which thus...
Electricity grids around the world are undergoing a fundamental transformation, thanks to the modernization of electricity distribution systems including smart meter deployment and applications. Data generated by smart meters provides a wealth of information that can help better understand and optimize the operation of electricity networks. This paper proposes a novel Hybrid Load Profile Clustering...
The sparsity and the problem of curse of dimensionality of high dimensional data make traditional clustering algorithms such as K-Means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) result in low quality clusters and increase the time complexity exponentially. Many Projected Clustering algorithms have been proposed to deal with noisy High Dimensional Data. However, most of...
In this paper, we introduce a new color image segmentation by using superpixels as feature representation and Manhattan Nonnegative Matrix Factorization (MahNMF) for accurate segmentation. Firstly, the image pixels are grouped into superpixels and considered as the coarse features. The next step is then conducted by factorizing the matrix feature into two nonnegative matrices, which respectively imply...
In this paper, we propose a new approach to fuzzy data clustering. We present a new algorithm, called TEDA-Cloud, based on the recently introduced TEDA approach to outlier detection. TEDA-Cloud is a statistical method based on the concepts of typicality and eccentricity able to group similar data observations. Instead of the traditional concept of clusters, the data is grouped in the form of granular...
In this paper an idea of using Evolving Fuzzy Model method for electrical energy consumption prediction is presented. The prediction of energy consumption is an important task for energy trading companies. The prediction should be as accurate as possible since the accuracy of the prediction translates directly into company's profit. In this paper we compare an adaptive linear model with evolving fuzzy...
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