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Clustering is a classic topic in optimization with k-means being one of the most fundamental such problems. In the absence of any restrictions on the input, the best known algorithm for k-means with a provable guarantee is a simple local search heuristic yielding an approximation guarantee of 9+≥ilon, a ratio that is known to be tight with respect to such methods.We overcome this barrier...
Clustering is an important tool for analyzing gene expression data. Many clustering algorithms have been proposed for the analysis of gene expression data. In this article we have clustered real life gene expression data via K-Means which is one of clustering algorithms. Also, we have proposed a new method determining the initial cluster centers for K-means. We have compared results of our method...
Indoor positioning systems have been actively studied so far, and recently, many researchers are adopting Wi-Fi signals for their systems. It is mainly because Wi-Fi networks are prevalent in the indoor environments these days; also it is more efficient than other methods as one can estimate his location by simply comparing current RSS (Received Signal Strength) with the fingerprint of Wi-Fi signals...
There are a diverse set of products for a particular type on the internet. When any user tries to find out best product among a certain type it is very much difficult to do it manually go through every one of them. That's why manually searching is not very efficient. In that scenario, recommendation system plays a great important role to recommend the best products. In this study, we develop a recommendation...
How to reduce the computation time and how to improve the quality of the clustering result are the two major research issues. Although several efficient and effective clustering algorithms have been presented, none of which is perfect. As such, an effective clustering algorithm, which is based on the prediction of searching information to determine the search directions at later iterations and employs...
Modelling and digitizing performing arts through motion capturing interfaces is an important aspect for the analysis, processing and documentation of intangible cultural heritage assets. However, existing modelling approaches may involve huge amounts of information which are difficult to process, store and analyze. To address these limitations, usually a skeleton describing the dancer motion is extracted...
The information to be transmitted along the nervous system is encoded with the rate of fire of the neurons expressing the number of action potentials in a temporal range. Findings from experimental studies in the development of visual prosthetic systems, as a neuroprosthetic device, are of critical importance. The determination of the various working intervals required for the development of electronic...
Collaborative learning is widely accepted as an approach to promote learning effectiveness and student satisfaction. However, the quality and outcomes of collaboration depend upon a number of factors, among which group formation plays an important role. Existing approaches take into account groups formed through random assignment or based on certain criteria such as academic performance, demographic...
With advances in technology, high volumes of a wide variety of valuable data of different veracity can be easily collected or generated at a high velocity in the current era of big data. Embedded in these big data are implicit, previously unknown and potentially useful information. Hence, fast and scalable big data science and engineering solutions that mine and discover knowledge from these big data...
K-means algorithm has been a workhorse of unsupervised machine learning for many decades, primarily owing to its simplicity and efficiency. The algorithm requires availability of two key operations on the data, first, a distance metric to compare a pair of data objects, and second, a way to compute a representative (centroid) for a given set of data objects. These two requirements mean that k-means...
The problem faced by the company is how to determine potential customers and apply CRM (Customer Relationship Management) in order to perform the right marketing strategy, so it can bring benefits to the company. This research aims to perform clustering and profiling customer by using the model of Recency Frequency and Monetary (RFM) to provide customer relationship management (CRM) recommendation...
The article focuses on the results of the research into scientific publications of the All-Russian Institute for Scientific and Technical Information of the Russian Academy of Sciences database (VINITI Database RAS) in different fields. The purpose of operation was to increase partition accuracy on the directions of large volumes of scientific data. This analysis was carried out on summaries of scientific...
k-Means clustering is one of the central problems in data analysis and remains as one of the most popular data processing algorithms. There are streaming variant of k-Means clustering algorithms to address the continuous and evolving data. Stream Computing allows users to capture and analyze all events and data, all the time and just in time. Streaming models have advantages over batch models due...
The Euclid distance based K-means clustering is among the hard classification algorithms. When dealing with deterministic remote sensing data, it is difficult to gain satisfactory classification results using K-means algorithm. The traditional K-means clustering algorithm is faced with several shortcomings such as locally converged optimization, being sensitive to initial clustering centers, etc....
Utilizing data for segmentation analysis can bring a streamlined way to get potential insight as of decision making support in a business organization. Using appropriate data analytical technique help the organizations in profiling their customer segments accurately. The result brings an effective marketing strategy. However, there are times in doing data analytic, the organization needs another variable...
Aiming at the problem that the node energy is limited, the network cycle is short and the throughput is low in the wireless sensor network, Dynamic Routing Protocol Based on Clustering was proposed known as KACO (K-means and Ant Colony Optimal). At the beginning it employs ant colony optimal and k-means algorithm to form cluster, and the novel method which consider the energy ratio, node location...
The last years, huge masses of data are produced or extracted by computational systems and independent electronic devices. To exploit this resource, novel methods must be employed or the established ones may be altered in order to confront the issues that arise. One of the most fruitful techniques, in order to locate and use information from data sources is clustering, and k-means is a successful...
In this paper we propose a segmentation algorithm for noisy Synthetic Aperture Radar (SAR) images. This method is based on k-means (KM) clustering and thresholding techniques. SAR images have huge employment in topography, remote sensing, and subsurface imaging. The segmentation of SAR images is always demanding because of the noise present in it. Speckle noise is the common noise present in SAR images...
Micro array data play a vital role in simultaneously monitoring the expression profile of large number of genes that are specified with various experimental conditions. In bioinformatics research, the recognition of co-expressed and coherent patterns is a major objective in micro array data analysis. The K-means clustering algorithm is gaining popularity in the knowledge discovery domain for effectively...
Scene detection via processing of multimedia data is a significant research area for the advancement of the video technologies and applications. Currently, the scene detection is mostly performed manually. Thus, it is time consuming and costly. Therefore, it is important to develop algorithms that can automatically segment scenes to support the advancement of these technologies and applications. With...
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