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Ultra-dense networks are regarded as a promising candidate for providing high data rate at low implementation cost. With a dense and irregular deployment of access points, spatial reuse will be improved, and varying cellular coverage also known as the amorphous cell is provided, which leads to a more complicated interference environment. In this paper, a novel interference coordination strategy, based...
In this digital world, we are facing the flood of data, but depriving for knowledge. The eminent need of mining is useful to extract the hidden pattern from the wide availability of vast amount of data. Clustering is one such useful mining tool to handle this unfavorable situation by carrying out crucial steps refers as cluster analysis. It is the process of a grouping of patterns into clusters based...
Classical methods in combinational logic circuits design are not appropriate in practice for designing new circuits, which have different gates and high number of inputs. On the other hand, evolutionary designs are good alternatives for combinational logic circuit design, but have a common drawback namely, high randomness of their cross-over method. In order to overcome this drawback, a new genetic...
This paper introduces the relative principium of K-Means algorithm, simulated annealing (SA) algorithm and particle swarm optimization (PSO) algorithm at first. Then, in allusion to the influence of the initial value of the K-Means algorithm on the optimal solution of the algorithm, a hybrid algorithm of K-Means based on SA-PSO is proposed. The new algorithm uses the advantage of jumping out of local...
In last two decades several educational institutes have started gaining momentum while many of them are in self financing mode. Every institute wants to have good student strength to allow a smooth academic session. This paper proposes the use of machine learning techniques in educational domain to enhance the quality of student admissions in any higher educational institute. The focus of this paper...
Bioinformatics is the science of managing, analyzing, extracting, and interpreting information from biological sequences and molecules. Recent advancements in microarray technology allow simultaneous monitoring of the expression levels of a large number of genes over different experiment conditions. Facing this huge amount of data, the biologist cannot simply use the traditional techniques in biology...
Cluster analysis is a principal method in analytics domain of data mining. The algorithm used for clustering directly influences the results obtained from applying the clustering algorithm (clusters). Data clustering is done in order to identify the patterns and trends not identifiable from just looking at the data. Clustering may be supervised (if the machine training data set is available) or unsupervised...
With extensive research of clustering algorithm, disadvantages of traditional K-means algorithm have been recognized. In order to overcome these flaws, a novel K-means clustering algorithm based on the modified shuffled frog leaping algorithm and simulated annealing algorithm is presented. In this approach, a new local searching strategy combined with the Particle Swarm Optimization algorithm is introduced...
With the rapid development of clustering analysis technology, there have been many application-specific clustering algorithms, such as text clustering. K-Means algorithm, as one of the classic algorithms of clustering algorithms, and a textual document clustering algorithms commonly used in the analysis process, is widely used because of its simple and low complexity. This article in view of two big...
Clustering is one of the most widely studied problem in machine learning and data mining. The algorithms for clustering depend on the application scenario and data domain. K-Means algorithm is one of the most popular clustering techniques that depend on distance measure. In this work, an extensive empirical evaluation of three significant variations of K-Means algorithm is carried out on the basis...
Nowadays, clustering has a major role in the most of research areas such as engineering, medical sciences, biology and data analysis. A popular clustering algorithm is K-means. This algorithm has advantages such as high speed and ease of employment, but it suffers from the problem of local optimal. In order to overcome this problem, a lot of Research has been done in clustering. In this paper we proposed...
k-means algorithm, in spite of its computational efficiency and capacity for faster convergence has some serious drawbacks like its tendency to stick into local optima and the requirement of supplying number of cluster before execution. Our algorithm used Differential Evolution (DE) as preprocessor to overcome those bottlenecks. Experiments show that the improved version of clustering algorithm produces...
The present article presents a novel and applied approach based on clustering to determine optimal locations, sizing of sub-transmission substations with their associated service area without determining location of candidate substations. The goal of this optimization is to minimize all of fixed costs and operation costs while all of constraints are met. Cost of equipment, construction, MV feeder...
Clustering is an exploratory data analysis technique, which categorizes the dataset into some groups. These groups are formed in a way so that items which have similar features live in same group and those have dissimilar features remain in other. There are many clustering algorithm available. Different kinds of algorithms are best used for different kinds of data. K-means is most used clustering...
This paper proposes the recommendation system which is a new method using k-means clustering of item category based on RFM(Recency, Frequency, Monetary) in u-commerce under ubiquitous computing environment which is required by real time accessibility and agility. In this paper, using a implicit method without onerous question and answer to the users, not used user's profile for rating to reduce customers'...
Clustering is one of the most popular methods for data analysis, which is prevalent in many disciplines such as image segmentation, bioinformatics, pattern recognition and statistics etc. The most popular and simplest clustering algorithm is K-means because of its easy implementation, simplicity, efficiency and empirical success. However, the real-world applications produce huge volumes of data, thus,...
In the rapid development of internet technologies, search engines play a vital role in information retrieval. To provide efficient search engine to the user, Link Based Search Engine for information retrieval using K-Means clustering algorithm has been developed. The traditional search engines provide users with a set of non-classified web pages to their request based on its ranking mechanism. In...
Paper introduces the 2-stage k-means algorithm which is faster than the standard 1-stage k-means algorithm. The main idea of the 2-stages is to move, in the first stage (fast), the centers of the clusters closer to their final locations. This will be done by using a small part of the data to achieve faster calculation. The next stage (slow) stage will start from the centers found during the first...
This paper describes a new revised clustering algorithm in which each cluster center derived from the revised mean of a subclass in previous recursion. This modification factors make up with the mean of the cluster center in previous recursion multiplied with a coefficient polynomial. This computing center formula is derived from Fisher criteria. Experimental results show that the proposed clustering...
Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data. In this paper, we improved the K-means algorithm. Our improvement is based on the triangle inequality...
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