Search Results - (( java implementation model algorithm ) OR ( using cluster learning algorithm ))
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1
Web-based clustering tool using fuzzy k-mean algorithm / Ahmad Zuladzlan Zulkifly
Published 2019“…This project will use fuzzy k-means clustering algorithm to cluster the data because it is easy to implement and have many advantages. …”
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Thesis -
2
AUTOMATED MODEL GENERATION OF FSM AND NUSMV MODEL FROM RSA JAVA SOURCE CODE FOR MODEL CHECKING
Published 2021“…RSA is one of these encryption algorithms that have been implemented in security systems. …”
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3
Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…Moreover, experiments demonstrate that final clustering solution generated by the proposed incremental genetic-based clustering ensemble algorithm using the pattern ensemble learning method possess comparative or better clustering accuracy than clustering solutions generated by the incremental genetic-based clustering ensemble algorithms using other recombination operators. …”
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4
Provider independent cryptographic tools
Published 2003“…The library is implemented by using Java cryptographic service provider framework that conforms to Java Cryptographic Architecture (JCA) and Java Cryptographic Extension (JCE). …”
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Monograph -
5
Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
Published 2018“…EM and K-means clustering algorithms are used to cluster the multi-class classification attribute according to its relevance criteria and afterward, the clustered attributes are classified using an ensemble random forest classifier model. …”
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6
Direct approach for mining association rules from structured XML data
Published 2012“…The thesis also provides a two different implementation of the modified FLEX algorithm using a java based parsers and XQuery implementation. …”
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7
Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…Phase 1 is mainly to evaluate the performance of clustering algorithm (K-Means and FCM). Phase 2 is to study the performance of proposed integration system which using the data clustered to be used as train data for Naïve Bayes classifier. …”
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8
An improved self organizing map using jaccard new measure for textual bugs data clustering
Published 2018“…One of the commonly used algorithm for bug clustering is K-means, which is considered a simplest unsupervised learning algorithm for clustering, yet it tends to produce smaller number of cluster. …”
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9
An improved self organizing map using jaccard new measure for textual bugs data clustering
Published 2018“…One of the commonly used algorithm for bug clustering is K-means, which is considered a simplest unsupervised learning algorithm for clustering, yet it tends to produce smaller number of cluster. …”
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10
Quantifying usability prioritization using K-means clustering algorithm on hybrid metric features for MAR learning
Published 2023Conference Paper -
11
Analysis of Chinese patents associated with incremental clustering algorithms: A review / Archana Chaudhari
Published 2022“…To achieve learning from such dynamic data sources, incremental clustering algorithms are used mandatorily. …”
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12
Optimization of blood vessel detection in retina images using multithreading and native code for portable devices
Published 2013“…The performance of Java programming model and native programming model are compared with respect to the execution time for blood vessel detection. …”
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Conference or Workshop Item -
13
Harmony Search-Based Fuzzy Clustering Algorithms For Image Segmentation
Published 2011“…Fuzzy clustering algorithms, which fall under unsupervised machine learning, are among the most successful methods for image segmentation. …”
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14
Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering
Published 2024“…To address this issue, this study incorporated joint graph learning from the gmc algorithm into swmcan, creating a new algorithm called swmcan-jg. …”
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15
Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
“…K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. …”
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16
MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
Published 2012“…K-Means is one of the unsupervised learning and partitioning clustering algorithms. It is very popular and widely used for its simplicity and fastness. …”
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17
The new efficient and accurate attribute-oriented clustering algorithms for categorical data
Published 2012“…Four real-life data sets obtained from University of California Irvine (UCI) machine learning repository and ten synthetically generated data sets are used to evaluate MGR and IG-ANMI algorithms, and other four algorithms are used to compare with these two algorithms. …”
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18
Reliability fuzzy clustering algorithm for wellness of elderly people
Published 2019“…Therefore, the implementation of z-numbers is taken into consideration, where it has more authority to describe the knowledge of human being and extensively used in uncertain information development. Thus, the objective of this paper is to propose a reliable fuzzy clustering algorithm using z-numbers. …”
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19
A Toolkit for Simulation of Desktop Grid Environment
Published 2014“…A simulator for desktop grid environment has been developed using Java as the implementation language due to its wide popularity. …”
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Final Year Project -
20
Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning
Published 2023“…As a conclusion, KMeans clustering was presenting better cluster results using this particular dataset.…”
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