Search Results - (( _ implementation clustering algorithm ) OR ( java application testing 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
Application-Programming Interface (API) for Song Recognition Systems
Published 2024“…An approach capable of recognizing an audio piece of music with an accuracy equal to 90% was further tested based on this result. In addition the implementation is done by algorithm using Java’s programming language, executed through an application developed in the Android operating system. …”
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3
RSA Encryption & Decryption using JAVA
Published 2006“…The implementation of this project will be based on Rapid Application Design Methodology (RAD) and will be more focusing on research and finding, ideas and the implementation of the algorithm, and finally running and testing the algorithm. …”
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Final Year Project -
4
The implementation of z-numbers in fuzzy clustering algorithm for wellness of chronic kidney disease patients
Published 2019“…Thus, there are two objectives of this paper; (i) to propose a reliable fuzzy clustering algorithm using z-numbers and; (ii) to cluster the Chronic Kidney Disease (CKD) patients based on the selected indicators to identify which cluster the patients belongs to (Cluster 0, Cluster 1, Cluster 2, Cluster 3 or Cluster 4) based on the membership functions defined. …”
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Conference or Workshop Item -
5
Image segmentation based on normalised cuts with clustering algorithm
Published 2013“…As the clusters initialisation gives influence to the segmentation result, optimisation of the clustering algorithm is implemented to achieve a better segmentation. …”
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6
Autonomous and deterministic supervised fuzzy clustering
Published 2010“…This algorithm implements k-means to initialize the fuzzy model. …”
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7
Enhanced Clustering Algorithms For Gray-Scale Image Segmentation
Published 2012“…The algorithms are chosen since they are easy to be implemented, required low computational time and less sensitive to noise and artifacts. …”
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8
Clustering for binary data sets by using genetic algorithm-incremental K-means
Published 2018“…The results show that GAIKM is an efficient and effective new clustering algorithm compared to the clustering algorithms and to the IKM itself. …”
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9
Parallelization of noise reduction algorithm for seismic data on a beowulf cluster
Published 2010“…The proposed algorithm has been implemented on an experimental Beowulf cluster which consists of 12 nodes operating on Linux Ubuntu platform. …”
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Citation Index Journal -
10
Improving on the network lifetime of clustered-based wireless sensor network using modified leach algorithm
Published 2012“…Meanwhile in LEACH, the cluster head selection was based on distributed algorithm. …”
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11
Cluster Analysis of Data Points using Partitioning and Probabilistic Model-based Algorithms
Published 2014“…Some clustering algorithms, especially those that are partitioned-based, clusters any data presented to them even if similar features do not present. …”
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12
A soft hierarchical algorithm for the clustering of multiple bioactive chemical compounds
Published 2007“…The results of the algorithm show significant improvement in comparison to a similar implementation of the hard c-means algorithm.…”
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Book Section -
13
MGR: An Information Theory Based Hierarchical Divisive Clustering Algorithm for Categorical Data
Published 2014“…This research proposes mean gain ratio (MGR), a new information theory based hierarchical divisive clustering algorithm for categorical data. MGR implements clustering from the attributes viewpoint which includes selecting a clustering attribute using mean gain ratio and selecting an equivalence class on the clustering attribute using entropy of clusters. …”
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14
The new efficient and accurate attribute-oriented clustering algorithms for categorical data
Published 2012“…Many algorithms for clustering categorical data have been proposed, in which attribute-oriented hierarchical divisive clustering algorithm Min-Min Roughness (MMR) has the highest efficiency among these algorithms with low clustering accuracy, conversely, genetic clustering algorithm Genetic-Average Normalized Mutual Information (G-ANMI) has the highest clustering accuracy among these algorithms with low clustering efficiency. …”
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15
Evaluation of FCV and FCM clustering algorithms in cluster-based compound selection
Published 2011“…Therefore, these two clustering algorithms are implemented and the performance is analyzed based on the effectiveness of the clustering results in terms of mean intercluster molecular dissimilarity (MIMDS) where these results are compared with one another. …”
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16
Comparison of Search Algorithms in Javanese-Indonesian Dictionary Application
Published 2020“…Performance Testing is used to test the performance of algorithm implementations in applications. …”
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17
A Comparative Study Of Fuzzy C-Means And K-Means Clustering Techniques
Published 2014“…First we present an overview of both methods with emphasis on the implementation of the algorithm. Then, we apply six datasets to measure the quality of clustering result based on the similarity measure used in the algorithm and its representation of clustering result. …”
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Conference or Workshop Item -
18
Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…These algorithms mostly built upon the partitioning k-means clustering algorithm. …”
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19
An improved pheromone-based kohonen self-organising map in clustering and visualising balanced and imbalanced datasets
Published 2021“…Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data.…”
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20
An Improved Pheromone-Based Kohonen Self- Organising Map in Clustering and Visualising Balanced and Imbalanced Datasets
Published 2021“…Therefore, this proposed algorithm can be implemented in clustering other complex datasets, such as high dimensional and streaming data.…”
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