pso k means for mining educational data set

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Text Documents Clustering using K-Means Algorithm

The K-Means algorithm aims to partition a set of objects, based on their attributes/features, into k clusters, where k is a predefined or user-defined constant. The main idea is to define k

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AN IMPROVED CLUSTERING ALGORITHM FOR CUSTOMER

In Eq. 1, n indicates the number of dimensions in the given data, x and y are the data in the data set, d(x, y) is the distance between data x and y. For the customer segmentation, the

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Text clustering on latent semantic indexing with PSO

The K- means algorithm iteratively refines a randomly chosen set of K initial centroids, minimizing the average distance The transformed LSI for document representation (maximizing the similarity) of documents to their closest (most similar) centroid.

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International Journal of Data Analysis Techniques and

Abstract To handle data sets with imbalanced classes is an exigent problem in the area of machine learning and data mining .Though a lot of work has been done by many researcher in the literature for two class imbalanced problems, multiclass problems still needs to be explored . Most of existing imbalanced learning techniques have proved to be

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Rule Discovery with Particle Swarm Optimization

data sets. Rule discovery task is formulized into an optimization problem data mining is a fairly new research area and needs much more work to do. So Particle Swarm Optimization (PSO), a new population-based evolutionary com-

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Predicting Student Performance A Statistical and Data

Predicting Student Performance A Statistical and Data Mining Approach V.Ramesh Assistant Professor Educational Data Mining, Decision Tree, Multilayer Perception, Student performance. 1. used FP Tree and K-means clustering

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DATA MINING IN HIGHER EDUCATION UNIVERSITY STUDENT

Authors in go a further step for acquiring data-set in educational domain, they depend on social networks to acquire relevant information that can be used in educational data mining tasks to improve the accuracy of classification models in comparison with usage of only demographic and academic attributes, e.g., students' age, gender, or

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Data clustering using modified fuzzy-PSO (MFPSO)

Hsiang-Chuan Liu, Jeng-Ming Yih, Der-Bang Wu, Shin-Wu Liu, Fuzzy C-Mean Clustering Algorithms Based on Picard Iteration and Particle Swarm Optimization, Proceedings of the 2008 International Workshop on Education Technology and Training 2008 International Workshop on Geoscience and Remote Sensing, p.838-842, December 21-22, 2008 [doi10

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Center of gravity PSO for Partitioning Clustering

However, PSO has a more accurate solution than k-means. proposed a dynamic PSO for clustering. They apply their algorithm in the area of image classification.

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A Review of Various Clustering Techniques Amazon S3

FPGA-accelerated Computer Cluster Data Mining to Banking Data This paper proposed an approach in which the k-means clustering algorithm is designed and implemented on an

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(PSO) based Dimensionality Reduction SERSC MAFIADOC.COM

where Average of ith feature in j th data set Average of ith feature in the whole data set No. of instances in j th data set i th feature of k th instance in j th data set 3.3. PSO Algorithm Particle swarm optimization is an optimization technique that finds a solution by several iterations.

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Training Artificial Neural Networks by a Hybrid PSO-CS

Training Artificial Neural Networks by a Hybrid PSO-CS Algorithm. ANN has become one of the most important data mining techniques, and can be used for both supervised and unsupervised learning. In fact, feedforward neural networks (FNNs) are the most popular neural networks in practical applications. means finding a set of weights and

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Algorithms 2015Jengfung Chen Quang Hung Do Honien HsiehArtificial neural network

Data mining and simulation a grey relationship demonstration

Data mining models based on such data are expected to be less accurate, but hopefully not by very much. However, grey related model input would be expected to

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International Journal of Systems Science 2006Desheng Wu David L Olson Zhao Yang Dong University of Science and Technology of China University of Nebraska Lincoln UniverInformation extraction Fuzzy logic Grey relational analysis Data mining Fuzzy set

Applying Data Mining Techniques in Property/Casualty

Applying Data Mining Techniques in Property~Casualty Insurance Lijia Guo, Ph.D., A.S.A. means, of large quantities of data in order to discover meaningful patterns and rules (Berry and Linoff, 2000). Assuming you have prepared a data set for mining, you then need to define the scope of your

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UCI Machine Learning Repository Iris Data Set

Data Set Information This is perhaps the best known database to be found in the pattern recognition literature. Fisher's paper is a classic in the field and is referenced frequently to this day.

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Education in Data Science Orange Data Mining

Education in Data Science. Oh, the richness and art of data mining Experimenting with k-Means Clustering. After the intro on k-means clustering algorithms (there is a widget from educational add on to support this), a great exercise for students is to check when the algorithm works and where it fails. for the smiley data set, k-means

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Orange3 Educational Add-on Documentation Read the Docs

Orange3 Educational Add-on Documentation, Release 0.1 Widgets in Educational Add-on demonstrate several key data mining and machine learning procedures. The widgets The aim of this widget is to show the working of ak-means clustering algorithmon two attributes from a data set. The widget applies k-means clustering to the selected two

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OBJECT EXTRACTION FROM LIDAR DATA USING AN

integrating the simplicity of the k-means algorithm with the capability of the artificial bee colony algorithm, a robust and efficient clustering method for object extraction from LIDAR data is

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A PSO-Based Subtractive Data Clustering Algorithm

A PSO-Based Subtractive Data Clustering Algorithm Free download as File (.pdf), Text File (.txt) or read online for free. There is a tremendous proliferation in the amount of information available on the largest shared information source, the World Wide Web.

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Data Mining In Excel Lecture Notes and Cases

data mining techniques for classiflcation, prediction, anity analysis, and data exploration and reduction. Installation Click on setup.exe and installation dialog boxes will guide you through the instal-

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BPSO Optimized K-means Clustering Approach for Data Analysis

the minimum sum of squared errors for a given data set is considered. Two evolutionary optimization algorithms BFO and PSO are combined to optimize KM algorithm to F-measure, K-means, Data Mining, etc. Keywords PSO (Particle Swarm Optimization), BFO (Bacterial Foraging

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Text Mining in Practice with R Data Mining Statistics

Make text mining an integral component of marketing in order to identify brand evangelists, impact customer propensity modelling, and much more Most companies' data mining efforts focus almost exclusively on numerical and categorical data, while text remains a largely untapped resource.

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K-Means Algorithm Unsupervised Learning Coursera

We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points. clusters we think there are in the data set. Coursera provides universal access to the world's best education, partnering with top

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Step-By-Step K-Means Example Mnemosyne Studio

Statistical Clustering. k-Means. View Java code. k-Means Step-By-Step Example. As a simple illustration of a k-means algorithm, consider the following data set consisting of the scores of two variables on each of seven individuals Subject A, B. This data set is to be grouped into two clusters. As a first step in finding a sensible initial partition, let the A B values of the two

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