Search Results - ((((((new algorithm) OR (based algorithm))) OR (mining algorithm))) OR (means algorithm))
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1
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
Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman
Published 2013“…The idea was incorporated into a new algorithm called, k-Approximate Modal Haplotypes (&-AMH) algorithm. …”
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3
A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
Published 2022“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan- Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
Published 2022“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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5
Validation on an enhanced dendrite cell algorithm using statistical analysis
Published 2017“…Evaluating a novel or enhanced algorithm is compulsory in data mining studies in order to measure it has superior performance than its previous version. …”
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6
A comparative effectiveness of hierarchical and nonhierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
Published 2022“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), HartiganWong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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7
A comparative effectiveness of hierarchical and non-hierarchical regionalisation algorithms in regionalising the homogeneous rainfall regions
Published 2022“…The results of the analysis show that Forgy K-means non-hierarchical (FKNH), Hartigan-Wong K-means non-hierarchical (HKNH), and Lloyd K-means non-hierarchical (LKNH) regionalisation algorithms are superior to other automated agglomerative hierarchical and non-hierarchical regionalisation algorithms. …”
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RMIL/AG: A new class of nonlinear conjugate gradient for training back propagation algorithm
Published 2018“…The results show that the computational efficiency of the proposed method was better than the conventional BP algorithm.…”
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9
Seed disperser ant algorithm for optimization / Chang Wen Liang
Published 2018“…The Seed Disperser Ant Algorithm (SDAA) is developed based on the evolution or expansion process of Seed Disperser Ant (Aphaenogaster senilis) colony. …”
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10
A customized non-exclusive clustering algorithm for news recommendation systems
Published 2019“…The experimental results demonstrated that the OC outperforms the k-means algorithm with respect to Precision, Recall, and F1-Score.…”
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USING LATENT SEMANTIC INDEXING FOR DOCUMENT CLUSTERING
Published 2010“…In this work, the LSI method is used to produce the patterns of terms, so that documents can be mapped into concept space. Based on the new representation, the documents are then subjected to the clustering algorithm itself, which is Fuzzy c-Means algorithm. …”
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12
Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…In the online phase, the stream clustering algorithm for trajectories based on the lifespan of the cluster is proposed (CC_TRS) to overcome the limitations of the time window technique. …”
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13
DATA CLASSIFICATION SYSTEM WITH FUZZY NEURAL BASED APPROACH
Published 2005“…The project's objective is identifying the available data mining algorithms in data classification and applying new data mining algorithm to perform classification tasks. …”
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Final Year Project -
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MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM
Published 2011“…In this paper we proposed an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products. …”
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15
MINING CUSTOMER DATA FOR DECISION MAKING USING NEW HYBRID CLASSIFICATION ALGORITHM
Published 2011“…In this paper we proposed an algorithm for mining patterns of huge stock data to predict factors affecting the sale of products. …”
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16
A web-based implementation of k-means algorithms
Published 2022“…The K-means algorithm has been around for over a century. …”
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Final Year Project / Dissertation / Thesis -
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Efficient prime-based method for interactive mining of frequent patterns.
Published 2011“…During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. …”
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An efficient fuzzy C-least median clustering algorithm
Published 2021“…In this paper we are discussing our new procedure for clustering called Fuzzy C-least median of squares algorithm which is an improvement to Fuzzy C-means (FCM) algorithm. …”
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A semi-apriori algorithm for discovering the frequent itemsets
Published 2014“…Furthermore, the present mining algorithms cannot perform efficiently due to high and repeatedly database scan. …”
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Conference or Workshop Item -
20
Genetic Algorithm for Web Data Mining
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