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This study investigates a novel technique of tissues segmentation of high-grade (HG) glioma. Segmentation of tumor and edema for treatment planning is crucial. Anisotropic diffusion filter removes the noise and preserves the tumor tissues in MRI images. K-mean clustering algorithm clusters the brain tissues in normal and tumor tissues. The healthy tissues surround tumor tissues. Hierarchical centroid...
This survey highlights issues in clustering which hinder in achieving optimal solution or generates inconsistent outputs. We called such malignancies as dark patches. We focus on the issues relating to clustering rather than concepts and techniques of clustering. For better insight into the issues of clustering, we categorize dark patches into three classes and then compare various clustering methods...
Density peak (DP) based clustering algorithm is a recently proposed clustering approach and has been shown to be with great potential. This algorithm is based on the simple assumption that cluster centers have high local density and they are relatively far from each other. This observation is used to isolate cluster centers from other data. By making use of the density relationship among neighboring...
The paper describes a new scalable algorithm called NSLP for high-dimension, non-stationary linear programming problem solving on the modern cluster computing systems. The algorithm consists of two phases: Quest and Targeting. The Quest phase calculates a solution for the system of inequalities defining the constraint system of the linear programming problem under the condition of the input data dynamic...
In semi administered bunching is one of the vital errands and goes for gathering the information objects into classes (groups) to such an extent that the similitude of items inside bunches is high and the comparability of articles between bunches is Less. The dataset once in a while might be in blended nature that is it might comprise of both numeric and unmitigated sort of information. So two types...
With wafer fabs running at near full capacity, it is a constant challenge to maintain high yields. Many different products are fabricated by the same equipment. So the sudden change in product yield, a yield excursion, can have a significant impact to many different products. Therefore, it is critical to detect an excursion as early as possible and fix the cause in order to minimize the impact. This...
We propose a new fully automatic spike sorting algorithm that is able to match, or even improve, the performance of semiautomatic solutions with supervised intervention from expert users. We achieved this by incorporating: 1) a set of heuristic criteria inspired by the expert actions following the solution from semiautomatic algorithms, and 2) an improved feature selection method that increases the...
With the advance of mobile electronic devices and the development of positioning technology, a large volume of spatio-temopral data are collected in the form of desultorily data streams, which contain a lot of potential information. In this study, we focus on discovering the composition relationships between observation moving objects in a long period. Such research can be widely used in military...
Detection of repetitive patterns in images is subject of several research papers. The majority of them deals with detection of lattice patterns of repetitive elements. However, there are many situations, when element's repetition doesn't follow any particular pattern. In this paper we focus on the following two objectives. Firstly, our algorithm detects repetitive elements regardless of their relative...
Clustering is a well-recognized data mining technique which enables the determination of underlying patterns in datasets. In electric power systems, it has been traditionally utilized for different purposes like defining customer load profiles, tariff designs and improving load forecasting. Some surveys summarized different clustering techniques which were traditionally used for customer segmentation...
Agricultural mechanization impacts on agricultural productivity and society development far-reaching. The emergence of VLSI (Very Large-Scale Integrated circuits) provides possibility for full intelligence and automation of agricultural products. The VLSI placement is now facing such double challenges: the integration scale and the circuit performance. From the experimental results, we find current...
Library based design and IP reuse have been previously proposed to speed up the synthesis for large-scale FPGA designs. However, previous library based design flow faces several unresolved challenges. Firstly, they may result in large waste area between the modules due to the difference in module sizes. While utilizing multiple ratio modules can help to reduce the waste area, pre-synthesis each module...
Data stream clustering is an active area of research in big data. It refers to clustering constantly arriving new data records and updating existing cluster patterns and outliers in light of the newly arriving data. Density-based algorithms for solving this problem have the promise for finding arbitrary shape clusters and detecting anomalies without prior knowledge of the number of clusters. In this...
In topology representations such as maps, carbon nanotubes, and cellular networks, neighboring cells are assigned different exclusive colors to represent different characteristics. In the previous research, based on the fourcolor theorem, an algorithm that can allocate exclusive channels among neighbors using only four channels in a polygonal cluster sensor network is proposed. The performance of...
Topological data analysis is a noble method to analyze high-dimensional qualitative data using a set of properties from topology. In this paper, we explore the feasibility of topological data analysis for mining social media data by investigating the problem of image popularity. We randomly crawl images from Instagram, convert their captions to 300 dimensional numerical vectors using Word2vec, calculate...
A cluster validity index is to evaluate the correct number of clusters when partitioning a dataset. In this paper, we propose a new cluster validity index based on two measures called dispersion and overlap for Gaussian-distributed clusters. The dispersion measure is used to estimate the situation of data spreading in a cluster. A small dispersion measure for a cluster means that data points are distributed...
Cluster analysis aims at classifying data elements into different categories according to their similarity. It is a common task in data mining and useful in various field including pattern recognition, machine learning, information retrieval and so on. As an extensive studied area, many clustering methods are proposed in literature. Among them, some methods are focused on mining clusters with arbitrary...
Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
Topics on clustering ensemble have attracted much attention in recent years. In many clustering ensemble frameworks, the simple partitional clustering methods, e.g., the most famous κ-means, are used as the ensemble's member “clusterers”, due to their low computational complexity. These ensemble approaches extend the scope of application of individual clustering algorithms, and improve the robustness...
With the recent developments in medicine and biology experiments a large amount of data is gathered in the form of multimedia elements (images, videos). Many algorithms have been developed and adapted based on the system of interest, and often the most challenging feature of the images may be used to facilitate a better analysis of the image. Herein, we developed an image analysis algorithm for quantification...
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