[PDF] Top 20 A Novel K-means-based Feature Reduction
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A Novel K-means-based Feature Reduction
... two feature reduction methods: feature extraction [1, 2, 3, 4, 5, 6, 7, 8] and feature selection [9, 3, 10, 11, 12, 13, 14, 15, 16, ...the feature extraction methods is principle ... See full document
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Crack Fault Classification for Planetary Gearbox Based on Feature Selection Technique and K-means Clustering Method
... vibration- based methods have been successfully used in the fault diagnosis and condition monitoring of rotating machin- ery, the appearance of faults in the analysis results has to be identified artificially, ... See full document
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A Novel Density based K-means Clustering for Test Case Prioritization in Regression Testing
... Abstract - In this paper, we work on improving the test case prioritization on the basis of clustering approach. A novel density based k-means clustering approach is used to make clusters of ... See full document
6
An improved density based k Means algorithm
... items, feature vectors or observation) into groups (clusters) which had been address in different contexts by many researchers in different domains across the ...hierarchical. K- means clustering ... See full document
6
Clustering based information retrieval with the aco and the k-means clustering algorithm
... the pre-processing of the documents. Then, the required features for the information retrieval are selected with the use of the ACO algorithm. Then, the features are subjected to the dynamic reduction scheme. ... See full document
6
A Power Attack Method Based on Clustering
... as feature points to attack based on K-means clustering, and used Euclidean distance to calculate the similarity between clustering and classification ... See full document
7
K-MEANS BASED MULTIMODAL BIOMETRIC AUTHENTICATION USING FINGERPRINT AND FINGER KNUCKLE PRINT WITH FEATURE LEVEL FUSION
... by means of continuously verifying the user’s presence [6, ...frequency based approach and hamming distance based matching ...fusion, feature level fusion, score level fusion and decision ... See full document
13
A Novel Feature Reduction Method in Sentiment Analysis
... on feature space in a sentiment analysis, while at the same time improving the accuracy of sentiment polarity ...other feature reduction methods on book and music ...this novel method has ... See full document
7
A novel intrusion detection method based on OCSVM and K-means recursive clustering
... proposed K−OCSVM is slightly bigger compared to a simple OCSVM ...simulation. Based on these observations we conclude that the system, performs a classiffication in a comparable time to that of a simple ... See full document
10
A Novel K means Clustering Algorithm for Large Datasets Based on Divide and Conquer Technique
... data reduction should be performed prior to applying the clustering techniques which is performing dimension reduction, and the main disadvantage is sacrificing the quality of ...dimensionality ... See full document
5
Image Retrieval Using Modified Haar Wavelet Transform and K Means Clustering
... Content Based Image Retrieval is to retrieve an image from the image database when given a query image. Query Image is the users target image for the searching process. CBIR systems operate in two phases: indexing ... See full document
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ANALYSIS OF EFFECTIVENESS OF USING SIMPLE QUEUE WITH PER CONNECTION QUEUE (PCQ) IN THE BANDWIDTH MANAGEMENT (A CASE STUDY AT THE ACADEMY OF INFORMATION MANAGEMENT AND COMPUTER MATARAM (AMIKOM) MATARAM)
... using K-means clustering technique. K-means clustering technique is used to cluster 3D mesh vertices into suitable or non-suitable watermark carrier based on feature ...from ... See full document
7
Distributed Cluster Based 3D Model Retrieval with Map Reduce
... standardization based SIFT feature were extracted from three projection views of a 3D model, and then the distributed K -means clus- ter algorithm based on a Hadoop platform was ... See full document
11
LOAD CURRENT CONTROL BASED ON LUENBERGER OBSERVER FOR THREE PHASE POWER CONVERTER SVPWM
... the reduction of computational load is six-fold, compared to the traditional k-means ...grid- based heuristic method to initialize the k cluster centroids [17], [18], ...selected ... See full document
8
Automated spike sorting algorithm based on Laplacian eigenmaps and k means clustering
... The Amplitude-Only feature set yielded the poorest performance with large sorting error percentages as demonstrated in Table 1. This may be due to electrode drift or complex-spike cells whilst in other cases, the ... See full document
14
Novel way of finding initial means in k means clustering and validation using WEKA
... Much of the work has been done for improving the efficiency, accuracy and stability of the k-means clustering algorithm. In [1], a method is proposed for computing a refined starting condition from a given ... See full document
5
Title: A Novel Kernel Based Fuzzy C Means Clustering With Cluster Validity Measures
... (Kernel based Fuzzy C Means) ...dimensional feature space, and then it will increases the possibility of linear seperability of the patterns in the feature space, then perform FCM in the ... See full document
7
Educational Data Clustering in a Weighted Feature Space Using Kernel K-Means and Transfer Learning Algorithms
... kernel k-means method, named Weighted kernel k-means (SFA), is proposed to discover the clusters of the similar students via their study performance in a weighted feature ...a ... See full document
10
A REVIEW ON DETECTION OF THUNDERSTORM USING DATA MINING AND IMAGE PROCESSING
... using K-means clustering based on various color factors in order to remove textures from original ...The feature extracted clustered image is analyzed further by applying wavelet ... See full document
10
Improving classification performance of k nearest neighbour by hybrid clustering and feature selection for non communicable disease prediction
... a novel NCDs prediction model to improve accuracy such as hybrid k-means as clustering technique, Weight SVM as feature selection technique and k-nearest neighbour as classifier ...that ... See full document
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