[PDF] Top 20 Kohonen Self Organizing Map with Modified K-means clustering For High Dimensional Data Set
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Kohonen Self Organizing Map with Modified K-means clustering For High Dimensional Data Set
... Kohonen Self Organizing Feature Maps, or SOMs provide a way of representing multidimensional data in much lower dimensional spaces - usually one or two ...a data compression ... See full document
6
Clustering Web Usage Data using Concept Hierarchy and Self Organizing Map
... using clustering algorithm and periodic user transaction for mining user behavior prediction for Web personalization ...Spherical K-Means algorithm for fast clustering of high ... See full document
7
Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self Organizing Map
... geospatial data and numerous applications of the technique in geospatial data analysis have proven to be successful (Takatsuka, 2001; Skupin and Hagelman, 2003; Koua et ...the clustering or ... See full document
13
Hand Gesture Recognition System Using Kohonen Self Organizing Map
... using Kohonen Self-Organizing Map (SOM), an artificial neural network which learns to classify data without ...A set of 29 hand gesture images representing letters of the ... See full document
8
Data Clustering and Topology Preservation Using 3D Visualization of Self Organizing Maps
... The Self Organizing Maps (SOM) is regarded as an excellent computational tool that can be used in data mining and data exploration ...a set of prototype vectors representing the ... See full document
6
NGTSOM: A Novel Data Clustering Algorithm Based on Game Theoretic and Self- Organizing Map
... central data in each cluster is considered as the new centroid and each data is assigned to the nearest ...the K-means algorithm. The Fuzzy C-means (FCM) algorithm [6], [7] puts forward ... See full document
10
An Application of Partitive Clustering Algorithm for Landslide Hazard Zonation
... [9][10], self-organizing map [12] and artificial neural network models ...in data, they are well suited for prediction or forecasting including sales forecasting, industrial process control, ... See full document
6
High Dimensional Data used in Consensus Neighbour Clustering with Fuzzy Based K-Means and Kernel Mapping
... High dimensional data clustering arises naturally in a lot of domains, and have regularly presented a great deal with for usual data mining ...techniques. Clustering becomes ... See full document
8
Application of Data Mining in predicting a Course for a Student Based on Previous Records, Financial Status and Personality Traits
... earlier, clustering is used in order to obtain useful knowledge from the ...class. Clustering is the process of making a group of abstract objects into classes of similar ...objects. Clustering is ... See full document
5
A modified kohonen self-organizing map (KSOM) clustering for four categorical data
... the map, and differentiate the clusters effectively ...and map. The KSOM can deal with incomplete and noisy data [8], and can transform a non-linear statistical data pattern in ... See full document
6
Kohonen Self Organizing for Automatic Identification of Cartographic Objects
... the Kohonen Self Organizing Map (KSOM) that uses competitive learning, which in turn results in data clustering ...input data vector is minimal for all neurons in the ... See full document
8
An Attempt to Recognize Handwritten Tamil Character Using Kohonen SOM
... We investigated a new representation of Tamil Character Recognition, and used Kohonen SOM techniques efficiently classifies handwritten and also for Printed Tamil characters. More effective and efficient feature ... See full document
5
Clustering pests of rice using self organizing map
... The SaM consists of a regular, usually two-dimensional (2D), grid of map units. The units are connected to adjacent ones by a neighbourhood relation. The number of [r] ... See full document
14
Towards model evaluation and identification using Self Organizing Maps
... model data items for the SOM ...measured data set and renders the SOM potentially useful for future model identification ...series data and which cannot be reduced to a rank ...discharge ... See full document
11
Improved k-means clustering using principal component analysis and imputation methods for breast cancer dataset
... This chapter discusses about the introduction of this research. The contents include information about missing value, and Principal Component Analysis for dimension reduction and K-means clustering. ... See full document
26
Performance Evaluation of K means Clustering Algorithm with Various Distance Metrics
... select k objects as initial cluster centers, then calculate the distance between each cluster center and each object and assign it to the nearest cluster, update the averages of all clusters, repeat this ...of ... See full document
5
Performance of Students Evaluation in Education Sector Using Clustering K-Means Algorithms
... unknown data, various methods and techniques were used such as the Association rules, pattern mining, classification technique, clustering technique, prediction, Supervised and unsupervised learning ... See full document
6
SOMSN: An Effective Self Organizing Map for Clustering of Social Networks
... In this paper, a new algorithm for detecting communities in social networks is presented. This algorithm is designed based on self-organizing map (SOM). The advantage of using SOM is that the method ... See full document
6
Adaptive K-Means Clustering Techniques For Data Clustering
... a set of disjoint clusters. K-means algorithm dependence on partition- based clustering technique is popular and widely used and applied to a variety of ...domains. K-means ... See full document
6
Big Data Analytics: Map Reduce Function using BIRCH Clustering Algorithm
... Performance Map-Reduce function when it uses K-Means clustering algorithm in shuffle function will take more amount of time, because the K-means clustering time complexity ... See full document
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