Search Results - (( data generation clustering algorithm ) OR ( using optimization method algorithm ))
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Discovering optimal clusters using firefly algorithm
Published 2016“…Existing conventional clustering techniques require a pre-determined number of clusters, unluckily; missing information about real world problem makes it a hard challenge.A new orientation in data clustering is to automatically cluster a given set of items by identifying the appropriate number of clusters and the optimal centre for each cluster.In this paper, we present the WFA_selection algorithm that originates from weight-based firefly algorithm.The newly proposed WFA_selection merges selected clusters in order to produce a better quality of clusters.Experiments utilising the WFA and WFA_selection algorithms were conducted on the 20Newsgroups and Reuters-21578 benchmark dataset and the output were compared against bisect K-means and general stochastic clustering method (GSCM).Results demonstrate that the WFA_selection generates a more robust and compact clusters as compared to the WFA, bisect K-means and GSCM.…”
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2
Incremental interval type-2 fuzzy clustering of data streams using single pass method
Published 2020“…The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. …”
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A new variant of black hole algorithm based on multi population and levy flight for clustering problem
Published 2020“…Black Hole (BH) optimization algorithm has been underlined as a solution for data clustering problems. …”
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5
Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…This strategy includes a number of components that are a novel approach to clustering generation. In fact a data clustering method is proposed consisting of separate outputs: (i) To select a relevant subset of original features based on our proposed algorithm; which is Enhanced Binary Particle swarm Optimization (EBPSO), (ii) To mine data using various data chunks (windows) and overcome a failure of single clustering. …”
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6
An enhanced version of black hole algorithm via levy flight for optimization and data lustering problems
Published 2019“…Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, and data clustering problem in particular. …”
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An enhanced version of black hole algorithm via levy flight for optimization and data clustering problems
Published 2019“…Recently, nature-inspired algorithms have been proposed and utilized for solving the optimization problems in general, and data clustering problem in particular. …”
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Social media mining: a genetic based multiobjective clustering approach to topic modelling
Published 2021“…This paper investigates the effects of using a multiobjective genetic algorithm (MOGA) based clustering technique to cluster texts for topic extraction which is designed based on the structure and purity of the clusters in order to determine the optimal initial centroids and the number of clusters, k. …”
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Efficient genetic partitioning-around-medoid algorithm for clustering
Published 2019“…In general, genetic based clustering algorithms showed the ability to reach near global optimal solution. …”
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10
Graph-Based Algorithm With Self-Weighted And Adaptive Neighbours Learning For Multi-View Clustering
Published 2024“…To address the noise problem in multi-view data, this study enhances the gbs method and develops a new self-weighted graph multi-view clustering algorithm (swmcan). …”
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11
Cluster merging based on weighted Mahalanobis distance with application in digital mammography
Published 1998“…Properties of the new algorithm are presented by examining the clustering quality for codebooks designed with the proposed method and another common method that uses Euclidean distance. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…The clustering algorithm consists of two components: the temporal micro-clusters generation and the temporal micro clusters merging. …”
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Electricity load profile determination by using fuzzy C-means and probability neural network / Norhasnelly Anuar
Published 2015“…The objectives of this project are to use FCM as the clustering algorithm to establish TLPs. …”
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14
Optimized feature construction methods for data summarizations of relational data
Published 2014“…This thesis also presents the study of a method to improve the descriptive accuracy of DARA algorithm by generating multi-instances of summarized data. …”
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15
An evolutionary based features construction methods for data summarization approach
Published 2015“…In other words, this research will discuss the application of genetic algorithm to optimize the feature construction process from the Coral Reefs data to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). …”
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Research Report -
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Customer mobile behavioral segmentation and analysis in telecom using machine learning
Published 2021“…Unsupervised machine learning algorithm K-means was used to cluster the data, and these results were analyzed and labeled with labels and descriptions. …”
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k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data
Published 2016“…Due to the growing amount of data generated and stored in relational databases, relational learning has attracted the interest of researchers in recent years.Many approaches have been developed in order to learn relational data.One of the approaches used to learn relational data is Dynamic Aggregation of Relational Attributes (DARA).The DARA algorithm is designed to summarize relational data with one-to-many relations. …”
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Book Section -
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Enhanced bibliographic data retrieval and visualization using query optimization and spectral centrality measure
Published 2019“…As the amount of data generated is growing exponentially, harnessing such voluminous data has become a major challenge these years especially bibliographic data. …”
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A Data Mining Approach to Enhancing Birth and Death Registration Processes
Published 2025“…The optimal number of clusters of clusters for birth and death data is determined as three using elbow and silhouette validation methods. …”
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Enhancement of new smooth support vector machines for classification problems
Published 2011“…To obtain optimal accuracy results, Uniform Design method is used to select parameter. …”
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