Search Results - (( generation classification clustering algorithm ) OR ( java simulation optimization algorithm ))
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1
Improving Classification of Remotely Sensed Data Using Best Band Selection Index and Cluster Labelling Algorithms
Published 2005“…This algorithm was compared to co-spectral plot method for labelling clusters the clusters generated in Landsat TM dataset. …”
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2
An ensemble data summarization approach based on feature transformation to learning relational data
Published 2015“…A genetic algorithm (GA) is also used to find the best centroids for all the clusters generated cluster centroids. …”
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3
Gas Identi cation by Using a Cluster-k-Nearest-Neighbor
Published 2009“…We find 98.7% of accuracy in the classification of 6 different types of Gas by using K-means cluster algorithm and we find almost the same by using the new clustering algorithm.…”
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4
A review: accuracy optimization in clustering ensembles using genetic algorithms
Published 2011“…It combines multiple partitions generated by different clustering algorithms into a single clustering solution. …”
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5
Modified archive update mechanism of multi-objective particle swarm optimization in fuzzy classification and clustering
Published 2022“…It was superior to the clustering algorithm methods in most real-world datasets with means ARI of over 0.35. …”
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6
A review on data stream classification
Published 2018“…As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. …”
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7
Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning
Published 2019“…Other types of the ART-based topological clustering algorithms have been developed, however, these algorithms have various drawbacks such as a large number of parameters, sensitivity to noisy data. …”
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8
A hybrid-based modified adaptive fuzzy inference engine for pattern classification
Published 2011“…A TSK type fuzzy inference system is constructed by the automatic generation of membership functions and rules by the hybrid fuzzy clustering and Apriori algorithm technique, respectively. …”
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9
Design and analysis of management platform based on financial big data
Published 2023“…In order to make financial big data generate business value and improve the information application level of financial management, aiming at the high error rate of current financial data classification system, this article adopts the fuzzy clustering algorithm to classify financial data automatically, and adopts the local outlier factor algorithm with neighborhood relation (NLOF) to detect abnormal data. …”
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10
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. The optimal number of cluster for FCM is obtained through cluster validity analysis. …”
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11
Enhanced AI-based anomaly detection method in the intrusion detection system (IDS) / Kayvan Atefi
Published 2019“…Based on the above components and circumstances, many studies have been performed on data clustering problems. Despite attempts to solve the data clustering issues, there are also many variants of modified algorithms in traditional information clustering that attempt to solve issues such as clustering algorithms based on condensation. …”
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12
Attribute reduction based scheduling algorithm with enhanced hybrid genetic algorithm and particle swarm optimization for optimal device selection
Published 2022“…The simulation is implemented with iFogSim and java programming language. …”
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13
A Review on Data Stream Classification
Published 2018“…As such the typical tasks of searching data have been linked to streams of data that are inclusive of clustering, classification, and repeated mining of pattern. …”
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14
Whale optimization algorithm based on tent chaotic map for feature selection in soft sensors
Published 2025“…The soft sensor was designed using several stages, including data collection, preprocessing, clustering, feature selection, and classification. The proposed TWOA achieved a higher fault classification result of 99.98% compared to other algorithms.…”
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15
Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing
Published 2011“…Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. …”
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16
Ant colony optimization algorithm for load balancing in grid computing
Published 2012“…The proposed algorithm is known as the enhance ant colony optimization (EACO). …”
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17
MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM
Published 2011“…The experimental result shows that the proposed hybrid k-mean plus MFP algorithm can generate more useful pattern from large stock data.…”
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18
An enhanced malay named entity recognition using clustering and classification approach for crime textual data analysis
Published 2018“…This Malay Named Entity Recognition (MNER) technique is proposed by using multi-staged of clustering and classification methods. The methods are Fuzzy C-Means and K-Nearest Neighbors Algorithm. …”
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19
Classification of metamorphic virus using n-grams signatures
Published 2020“…This research was conducted using Second Generation virus dataset. The first step is the classification model to cluster the metamorphic virus using TF-IDF technique. …”
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20
k-nearest neighbour using ensemble clustering based on feature selection approach to learning relational data
Published 2016“…However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset.A feature selection process can be introduced to overcome this problem.These selected features can be further optimized to achieve a good classification result.Several clustering runs can be performed for different values of k to yield an ensemble of clustering results. …”
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