[PDF] Top 20 A Comparative Study on K-Means And Genetic Algorithm For Data Clustering
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A Comparative Study on K-Means And Genetic Algorithm For Data Clustering
... this comparative study K-means & GA techniques are used to find out the support, confidence, memory space and time in seconds of Mushroom, Soyabean and Fishers Iris ...to ... See full document
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A Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
... to data clustering proposed by the authors are ...annealing algorithm for solving the clustering ...a clustering algorithm using genetic algorithm for improving ... See full document
14
Hybrid optimization for k-means clustering learning enhancement
... for clustering problems with high error, high intra cluster distance and low accuracy rate since the result is sensitive to the selection of initial cluster centers and this converges simply to local ...the ... See full document
47
Improvement in performance of k-means clustering algorithm using genetic algorithm based centroid selection
... The data mining techniques are classified into the supervised and unsupervised learning techniques In this presented work the unsupervised learning is the main area of investigation and algorithm ... See full document
6
Clustering for binary data sets by using genetic algorithm incremental K means
... of data that were collected by individuals, organizations or either firms has triggered the initiative to process and analyse this type of ...scaling, clustering and classification. Clustering and ... See full document
6
Case Study on Static k Means Clustering Algorithm
... static k-means clustering algorithm on sample data set and large data set with 1000 records German credit risk assessment data set in Weka data mining ...of ... See full document
8
Determining a Cluster Centroid of K-Means Clustering Using Genetic Algorithm
... of data mining that served to define clusters (groups) of the object in which objects are in one cluster have in common with other objects that are in the same cluster and the object is different from the other ... See full document
5
Comparative Study between K Means and K Medoids Clustering Algorithms
... better clustering, homogeneity or similarity should be greater within the group and the difference should be more between the different ...Various clustering algorithms have been developed for different ... See full document
6
Performance Evaluation of K means Clustering Algorithm with Various Distance Metrics
... pure clustering or hybrid ...Efficient K-means Clustering Algorithm Using Simple Partitioning presented by Ming-Chuan Hung, Jungpin Wu+, Jin-Hua Chang, In this paper an efficient ... See full document
5
Clustering Behavioral Data for Advertising Purposes using K Prototypes Algorithm
... this study the customer segmentation will be carried out using the k-prototype algorithm ...the clustering process is taken from the result of a survey conducted towards teens aged 12 to 17 in ... See full document
6
COMPARISON AND EVALUATION OF CLUSTER BASED IMAGE SEGMENTATION TECHNIQUES.
... comparison study has been performed among five clustering algorithms viz., K-Means partitioning algorithm, enhanced K-Means algorithm, Fuzzy c-Means ... See full document
10
Application of Factor Analysis to k means Clustering Algorithm on Transportation Data
... for data compression and visualization of high dimensional ...several data analysis techniques like Principal Components Analysis (PCA), Factor Analysis, cluster analysis may give insight into the patterns ... See full document
7
Comparative Study of Clustering Algorithms: Filtered Clustering and K-Means Cluttering Algorithm Using WEKA
... K-means clustering technique [24] is one of the simplest unsupervised learning techniques that aim to partition n observations into k clusters in which each observation belongs to the cluster ... See full document
10
Clustering K-Means Optimization with Multi- Objective Genetic Algorithm
... — K-Means is one of the partitioned clustering techniques where each cluster is represented by its mean ...Multi-objective genetic algorithm with Pareto rank approach can be used to ... See full document
6
A Comparative study on data mining clustering...
... Data clustering, in the simplest of its meaning is to cluster or group together relevant data which are similar in its properties or ...the data points based on the similarities they possess ... See full document
5
Enhanced K-Means Clustering Algorithm Using Collaborative Filtering Approach
... centroids. K-means cluster analysis is not recommended if you have too many explicit ...different clustering algorithm that can handle them better. K-means clustering that ... See full document
6
Big Data Analytics: Map Reduce Function using BIRCH Clustering Algorithm
... Big Data information is represented in unstructured form and NoSQL is used for query ...of data also too large and simple Query processing is not sufficient and ...of data, extracting the ... See full document
8
Clustering in Big Data Using K Means Algorithm Ajitesh Janaswamy
... proposed algorithm is higher than the k-means and other contemporary popular clustering ...proposed algorithm does not reject any ...hierarchical clustering can be used along ... See full document
6
K means Clustering Algorithm Based on E Commerce Big Data
... simple k-means clustering algorithm with an appropriate ...the k-means clustering ...the clustering algorithm and we have used Euclidean distance for ... See full document
5
High Dimensional Data used in Consensus Neighbour Clustering with Fuzzy Based K-Means and Kernel Mapping
... consensus clustering methods, namely the K-means-based algorithm, the graph partitioning algorithm (GP), and the hierarchical algorithm (HCC), were employed for the comparison ... See full document
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