Search Results - (( pattern clustering algorithm ) OR ( patterns ((ant algorithm) OR (acs algorithm)) ))*
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An improved ACS algorithm for data clustering
Published 2020“…Ant colony optimisation for clustering (ACOC) is a swarm algorithm inspired by the foraging behaviour of ants. …”
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Big data clustering using grid computing and ant-based algorithm
Published 2013“…This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm.…”
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Hybrid ant colony system algorithm for static and dynamic job scheduling in grid computing
Published 2015“…The algorithms are ACS(GA), ACS+GA, ACS(TS), and ACS+TS. …”
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An efficient fuzzy clustering algorithm for mining user session clusters on web log data
Published 2021“…This paper proposes an efficient Fuzzy Clustering algorithm for mining client session clusters from web access log information to find the groups of client profiles. …”
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Regional precipitation trend analysis at the Langat River Basin, Selangor, Malaysia
Published 2014“…In order to identify the homogeneous regions respectively for the annual and seasonal scales, firstly, at-site mean total annual and separately at-site mean total seasonal precipitation were spatialized into 5 km × 5 km grids using the inverse distance weighting (IDW) algorithm. Next, the optimum number of homogeneous regions (clusters) is computed using the silhouette coefficient approach. …”
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Filtering of Background DNA Sequences Improves DNA Motif Prediction Using Clustering Techniques
Published 2013“…Noisy objects have been known to affect negatively on the performance of clustering algorithms. This paper addresses the problem of high false positive rates in using self-organizing map (SOM) for DNA motif prediction due to the noisy background sequences in the input dataset. …”
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Anomaly detection of intrusion based on integration of rough sets and fuzzy c-means
Published 2005“…Fuzzy c-Means allow objects to belong to several clusters simultaneously, with different degrees of membership. …”
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Incremental continuous ant colony optimization for tuning support vector machine’s parameters
Published 2013“…Support Vector Machines are considered to be excellent patterns classification techniques. The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and Ant Colony Optimization can be used to overcome the difficulty. …”
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Optimizing support vector machine parameters using continuous ant colony optimization
Published 2012“…Support Vector Machines are considered to be excellent patterns classification techniques.The process of classifying a pattern with high classification accuracy counts mainly on tuning Support Vector Machine parameters which are the generalization error parameter and the kernel function parameter.Tuning these parameters is a complex process and may be done experimentally through time consuming human experience.To overcome this difficulty, an approach such as Ant Colony Optimization can tune Support Vector Machine parameters.Ant Colony Optimization originally deals with discrete optimization problems. …”
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Clustering ensemble learning method based on incremental genetic algorithms
Published 2012“…In the first and second phases, a threshold fuzzy c-means clustering algorithm as a clusterer and a pattern ensemble learning method based on the incremental genetic-based algorithms are proposed respectively. …”
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Integration Of Unsupervised Clustering Algorithm And Supervised Classifier For Pattern Recognition
Published 2017“…However, further study is needed in the feature extraction and clustering algorithms part as the performance of the pattern classification is still depending on the data input.…”
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Ant system-based feature set partitioning algorithm for classifier ensemble construction
Published 2016“…All of these approaches attempt to generate diversity in the ensemble.However, classifier ensemble construction still remains a problem because there is no standard guideline in constructing a set of accurate and diverse classifiers. In this study, Ant system-based feature set partitioning algorithm for classifier ensemble construction is proposed.The Ant System Algorithm is used to form an optimal feature set partition of the original training set which represents the number of classifiers.Experiments were carried out to construct several homogeneous classifier ensembles using nearest mean classifier, naive Bayes classifier, k-nearest neighbor and linear discriminant analysis as base classifier and majority voting technique as combiner. …”
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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
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Fast and Accuracy Control Chart Pattern Recognition using a New cluster-k-Nearest Neighbor
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Ant colony algorithm for text classification in multicore-multithread environment / Ahmad Nazmi Fadzal
Published 2017“…Pheromone concept is the main criterion that distinguish ACO to other algorithms. Based on the concept, pheromone saturation is used to combine stackable solution pattern that is discovered while straying to different term node to build a path. …”
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The Effect Of Linkages In The Hierarchical Clustering Of Auto-Regressive Algorithm For Defect Identification In Heat Exchanger Tubes
Published 2019“…The clustering results from the ‘ward’ linkages were represented via a dendrogram showing the hidden pattern between clusters. …”
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Expectation maximization clustering algorithm for user modeling in web usage mining system
Published 2009“…The results also indicate that kind of behavior given by EM clustering algorithm has improved the visit-coherence (accuracy) of navigation pattern mining.…”
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