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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...
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...
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...
This paper presents a method for hidden pattern mining on dental medical records related to oral conditions and different procedures that are performed on various patients. The decision to follow a set of procedures is based on the examination and diagnostics. Nowadays there is an increasing trend towards digital dentistry, but the full potential of digital data is not yet exploited because of several...
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...
Fuzzy optimization based Data clustering is one of the important data mining tool, which is dynamic research of real world problems. K-Means algorithm is the most popular clustering method, because it is very easy to implement and fast working in the most of the situation. However this K-means algorithm is sensitive to initialization and easily trapped in local optima. Particle swarm optimization...
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...
Association rules are adopted to discover the interesting relationship and knowledge in a large dataset. Knowledge may appear in terms of a frequent pattern discovered in a large number of production data. This knowledge can improve or solve production problems to achieve low cost production. To obtain knowledge and quality information, data mining can be applied to the manufacturing industry. In...
This paper extracted the basic energy consumption situation in 1 ton of aluminum production, for the current aluminum industry from mining to the aluminum processing. For a lot of multi-dimensional energy data, this paper based on the principle of K-means algorithm, and introduced into the error limit, then completed the data mining of the multi-dimensional energy consumption data of the aluminum...
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...
This paper presents an applied study in data mining and knowledge discovery. It aims at discovering patterns within historical students' academic and financial data at UST (University of Science and Technology) from the year 1993 to 2005 in order to contribute improving academic performance at UST. Results show that these rules concentrate on three main issues, students' academic achievements (successes...
The development of mobile network technology provides a great potential for social networking services. This paper studied data mining for social network analysis purpose, which aims at find people's social network patterns by analyzing the information about their mobile phone usage. In this research, the real database of MIT's Reality Mining project is employed. The classification model presented...
Currently, many governments are actively promoting implementation of ICT to be more citizen-oriented. For effective citizen relationship management, it is important to identify the needs of different citizen groups and to provide respective services for each group accordingly. In this way, the application of data mining tools would be very useful to understand citizen's needs. In this paper, focusing...
The way of collecting sensor data will face a revolution when newly developing technology of sensor network will become fully functional. The program/stack memory and the battery life of sensor nodes are not suitable for complex data mining in runtime. Effective data mining can be implemented on the central base station, where the computational power is not generally constrained. Real-world sensor...
The paper proposed a clustering method of decade observation data based on k-means algorithm, which adjusted the weight influence to similarity function by the missing values handling and scaling of range fields. This paper discussed the way to select initial cluster centers and the process of calculating cluster centers and assigning records to clusters. The test indicated the k-means algorithm had...
In this paper, we propose a memetic algorithm (MA) for classifier optimization based on a clustering method that applies the k-means algorithm over a specific derived space. In this space, each classifier or individual is represented by the set of the accuracies of the classifier for each class of the problem. The proposed sensitivity clustering is able to obtain groups of individuals that perform...
Clustering is a technique that can divide data objects into meaningful groups. Particle swarm optimization is an evolutionary computation technique developed through a simulation of simplified social models. K-means is one of the popular unsupervised learning clustering algorithms. After analyzing particle swarm optimization and K-means algorithm, a new hybrid algorithm based on both algorithms is...
A new clustering algorithm is proposed based on particle swarm optimization (PSO). The main idea of the new algorithm is to solve clustering problem using the fast search ability of the particle swarm optimization, each particle is composed of a cluster center vector, and represents a possible solution of the clustering problem. To escape from local optimum, a new idea is proposed, that is the neighborhood...
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