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In this paper, a new algorithm for visualization of high-multidimensional data is described. The algorithm follows several steps. At first, centers representing several categories are selected, and Euclidean distances between these centers are calculated in a high-dimensional space. Then these centers are placed in a 2-dimensional space in such a way that distances in this 2-dimensional space are...
In this paper, we proposed a novel medical images based computer aided diagnosis method named ECARMI. It combines the cost-sensitive learning with selective ensemble techniques to improve the medical images based diagnosis performance. At first, selective cost-sensitive SVM ensemble is utilized to perform the classification of medical images. Then, the Regions of Interest (ROIs) in positively identified...
In applications of personalized recommendation, user similarity of common clustering algorithms only considers user relationship without considering relationship between users and items, the similarity above reduces the accuracy of clustering, making it difficult to find similar users, and the same with item similarity. This paper improves the distance function of data clustering algorithm by Hamming...
It has been planned that the whole region of Slovak Republic's surface would be scanned, and there arose a need for storing the resulting data and making it publicly available. For this purpose, a scalable file-based database system for storing and accessing a large amount of geographic point cloud data was developed. The principle of the system was tested and proved to be sufficient in most situations,...
This paper presents the Candidate Moves Method for parallelization of the MiniMax search procedure. As the framework for this new method the basic MiniMax procedure is defined. The original modification of the classic MiniMax procedure is presented in detail. All of these theoretical results and novelties are successfully implemented and verified in authors' chess application Achilles, which is the...
With the advance in technology, the computer storage will become cheaper for the larger sizes. Previously, it allows the user to store more data at a lower cost. In context of digital forensic investigation, the traditional approach such as analysis on the hard disk will become inefficient in handling the huge data that is stored within it. The research on retrieving the open files from computer memory...
This paper presents a new clustering algorithm, called Cell-MST-Based Method that is a combination of a Cell-based method and Minimum Spanning Tree based (MST-based) methods. The algorithm is dedicated for Big Datasets on a limited memory computer, especially for thin big datasets which have a small number of attributes but a very large number of instances. Firstly, a Cell-based method converts a...
Nonnegative matrix factorization (NMF) is a powerful technique for dimensionality reduction. Conventional NMF algorithms usually keep the matrices W and H nonnegative while iterating. However, to get the NMF of a matrix, it's unnecessary to force the temporary solutions in iterations nonnegative. In this paper, we propose a two-staged approach for NMF. At the relaxation stage, the nonnegative constraint...
With the development of digital cable interactive business and the diversification of the customers' demand, grouping TV programmes based on preferences of users effectively is vital for market segmentation and differentiation. The study summarizes the main principle and characteristic of clustering algorithm, and uses K-Means algorithm to show TV programmes preference grouping based on 52392 subscribers...
I will describe a decade-long, multi-disciplinary, multi-institutional effort spanning neuroscience, supercomputing, and nanotechnology to build and demonstrate a brain-inspired computer and describe the architecture, programming model, and applications. I will also describe future efforts to build, literally, "brain-in-a-box". For more information, see: modha.org.
K-means is one of the most significant clustering algorithms in data mining. It performs well in many cases, especially in the massive data sets. However, the result of clustering by K-means largely depends upon the initial centers, which makes K-means difficult to reach global optimum. In this paper, we developed a novel algorithm based on finding density peaks to optimize the initial centers for...
The user enters any query to find desired information. To discover number of user search goals and representing each goal with some keyword, we first infer user search goals for a query by clustering feedback sessions. For that, we use a concept of pseudo document, which is the revised version of feedback session. Then the user search goals are determined by clustering the pseudo documents and it...
Data mining is one of the most exciting fields of research for the researcher. As data is getting digitized, systems are getting connected and integrated, scope of data generation and analytics has increased exponentially. Today, most of the systems generate non-stationary data of huge, size, volume, occurrence speed, fast changing etc. these kinds of data are called data streams. One of the most...
Now a days Internet Technology leads to the expansion of new class of network called as Wireless Mesh Networks (WMNs). Which contains mesh topology arrange across different district performing various tasks such as data aggregation, communication etc. Energy is very big issue in Wireless Network. So, in this paper we used the Two Hop Clustering based approach which optimizes the energy in Wireless...
Wireless Sensor Network (WSN) consists of small nodes with sensing, computation, and communications capabilities. Sensor node senses the data and sends data to the base station for further processing. These sensor nodes mainly rely upon batteries for energy, which get drained at a quicker rate due to the computation and communication and this decreases the lifetime of the network. In order to solve...
Directional Sensor Network (DSNs) being a subdivision of WSN has attracted researchers a lot due to its wide deployment in visual monitoring applications as the continuous technical advancements have enabled us making use of low-cost camera sensors. But because of the inherent random deployment of these camera sensors, the effective area coverage is reduced. Therefore, the effective area coverage...
With large companies and corporations becoming increasingly responsible for data collection, in recent years, a growing number of scientists have proposed using a variety of algorithms and different theories to solve the database problem. Even though existing solutions are effective in many cases many, problems are left to solve during the integration of database. The entity resolution (ER) is a crucial...
Teaching Quality Evaluation is an important process in all higher education institutes and is carried out after each semester. However, it is not fully reliable as students don't always fill the form with no bias whatsoever. The problem is too complex to be solved using present evaluation system. So, the main purpose of this paper is to show how to use machine learning techniques so that the system...
Centric Query Processing in Heterogeneous Wireless Sensor Network is the new emerging area of wireless sensor network which provides better interaction with physical world in effective and efficient manner. Previous work provides interaction between homogeneous wireless sensor network. In this paper, we have proposed a centric query processing approach which provides effective communication between...
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