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Predicting the performance of the students and helping them to improve their knowledge in subjects is one of the jobs of the educational universities. It is a laborious work to track many students in the universities. So, the universities started using content management systems to track the record of the student's marks, grades and performance. Even then, the tutor have to evaluate manually to finalize...
Feature selection or variable reduction is a fundamental problem in data mining, refers to the process of identifying the few most important features for application of a learning algorithm. The best subset contains the minimum number of dimensions retaining a suitably high accuracy on classifier in representing the original features. The objective of the proposed approach is to reduce the number...
The traditional K-means algorithm is sensitive to the initial center, and equates the importance of dimension data for multidimensional data. So it is unable to block the effects of dimensional data dimension, nor can it well reflect the influence of each dimension of clustering. The semi-supervised clustering introduces a small amount of sample points, so that it can significantly reduce the number...
The k-means algorithm is known to have a time complexity of O (n 2), where n is the input data size. This quadratic complexity debars the algorithm from being effectively used in large applications. In this article, an attempt is made to develop an O (n) complexity (linear order) counterpart of the k-means. The underlying modification includes a directional movement of intermediate clusters and thereby...
Diagnosis of the disease is one of the application areas where data mining techniques helps in the extraction of knowledge from medical database. Recently, researchers have been investigating the effect of cascading more than one technique showing enhanced results in the diagnosis of the disease. This paper proposes a hybrid model using K-means as a preprocessing algorithm. The proposed model is developed...
This paper presents an overview of the current state-of-the-art in mobile data stream mining and its applications. The paper presents the strategies and techniques for adaptation that are essential in order to perform real-time, continuous data mining on mobile devices. We present an overview of adaptation strategies for data stream mining and in particular for memory conservation with Algorithm Output...
In order to identify the reasons behind the hate crimes in Diyarbakir, Turkey a data mining research has been made. Various data mining algorithms are applied to data set containing forty big cases happened between 2009 and 2010. some algorithms helped us to model criminality in Diyarbakir, we learned which features for hatred crime features are important while dealing with cases. There has been also...
In this paper, we propose a hybrid intrusion detection system that combines k-Means, and two classifiers: K-nearest neighbor and Naïve Bayes for anomaly detection. It consists of selecting features using an entropy based feature selection algorithm which selects the important attributes and removes the irredundant attributes. This algorithm operates on the KDD-99 Data set; this data set is used worldwide...
Microarrays are made it possible to simultaneously monitor the expression profiles of thousands of genes under various experimental conditions. Identification of co-expressed genes and coherent patterns is the central goal in microarray or gene expression data analysis and is an important task in bioinformatics research. In this work the unsupervised Gene selection methods and CCIA with K-Means algorithms...
Real world applications are increasingly growing in the field of science and engineering, where data mining is an important stage to relate research and applications. Data objects are clustered based on the similarity using unsupervised learning techniques. The incomplete, noisy and inconsistent data may slow down the knowledge discovery in database process. Data preprocessing techniques improve the...
Patterns and classification of stock or inventory data is very important for decision making and business support. In this paper we proposed an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products. Identification of sales patterns from inventory data indicate the market trends which can further be used for forecasting, decision making and strategic planning...
In this paper, we propose a new data mining algorithm, which is used in surveillance video of stationary places. The algorithm combines Background Subtraction with Symmetrical Differencing in order to extract moving targets. According to the amount of motions occurring in video frames, we divide the video into different segments. Video segments are clustered via the improved K-Means algorithm. Then...
Clustering is the process of grouping a set of objects into classes. The clustering problem has been addressed by researchers in many contexts and disciplines. First, a process model for data mining and the typical requirements of clustering methods have been described. Second, the k-means algorithm and its advantages and disadvantages are introduced. Then the Iris dataset is used to specify the k-means...
This paper analyses the users' group interests by mining the internet browsing history. To count the visiting information of the interests' categories, visiting time and the number of users, get to the regularity of conclusion. Then, it has put forward an improved HAC (hierarchical agglomerative clustering) and k-means algorithm to cluster the users by their interests, to mine the users' access mode...
Data mining has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data". Clustering is the automated search for group of related observations in a data set. The K-Means method is one of the most commonly used clustering techniques for a variety of applications. This paper proposes a method for making the K-Means algorithm...
Clustering or data grouping is a key initial procedure in image processing. In present scenario the size of database of companies has increased dramatically, these databases contain large amount of text, image. They need to mine these huge databases and make accurate decisions in short durations in order to gain marketing advantage. As image is a collection of number of pixels. It is difficult to...
Most of the clustering algorithms perform loosely when dimensionality of the data set increase because some dimensions contain irrelevant or noisy data and randomly initialization of clusters centres gives the local optimum clustering. In this paper, we proposed a technique for reducing the effect of high dimensionality and randomly initialization of clusters centres. It consists of three phases....
This paper applies such data mining techniques as clustering and classifying to customer segmentation based on insurance Customer Risk Contribution matrix. A solution for segmentation management based on Clementine is put forwarded. It is brought forward that the insurance customer segmentation method, which can provide decision bases for insurance companies' making premium rate and controlling claim...
IDS (Intrusion Detection system) is an active and driving defense technology. This paper mainly focuses on intrusion detection based on data mining. The aim is to improve the detection rate and decrease the false alarm rate, and the main research method is clustering analysis. The algorithm and model of ID are proposed and corresponding simulation experiments are presented. Firstly, a method to reduce...
With the continued expansion of network resources and the rapid change of old and new information, the traditional information retrieval is difficult to adapt to the need of management of mass electronic data. It is a very important aspect of how to locate the information you want conveniently and accurately and improve the efficiency of search engines.
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