Search Results - (( java application interface algorithm ) OR ( learning information clustering algorithm ))
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Topological Clustering via Adaptive Resonance Theory With Information Theoretic Learning
Published 2019“…This paper proposes a topological clustering algorithm by integrating topological structure and information theoretic learning, i.e., correntropy, into adaptive resonance theory (ART). …”
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Analysis of Chinese patents associated with incremental clustering algorithms: A review / Archana Chaudhari
Published 2022“…To achieve learning from such dynamic data sources, incremental clustering algorithms are used mandatorily. …”
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Nature inspired data mining algorithm for document clustering in information retrieval
Published 2014“…Document clustering is an important technique that has been widely employed in Information Retrieval (IR). …”
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Book Section -
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Reliability fuzzy clustering algorithm for wellness of elderly people
Published 2019“…By gleaning insights from the data, the fuzzy clustering can learn from data, identify patterns and make decisions with minimal human intervention. …”
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Conference or Workshop Item -
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Classification of JPEG files by using extreme learning machine
Published 2018“…This paper proposes an Extreme Learning Machine (ELM) algorithm to assign a class label of JPEG or Non-JPEG image for files in a continuous series of data clusters. …”
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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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Thesis -
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Extreme learning machine classification of file clusters for evaluating content-based feature vectors
Published 2018“…The files are allocated in a continuous series of clusters. The ELM algorithm is applied to the DFRWS (2006) dataset and the results show that the combination of the three methods produces 93.46% classification accuracy.…”
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Modelling kernel methods for unsupervised learning of micro array data
Published 2008“…Unsupervised learning, mostly represented by data clustering methods, is an important machine learning technique. …”
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Monograph -
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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 -
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Clustering Based on Customers’ Behaviour in Accepting Personal Loan using Unsupervised Machine Learning
Published 2023“…Focusing on clustering algorithms, the study employs popular methods like K-Means Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Agglomerative Hierarchical Clustering, and Mean Shift Clustering to understand customer characteristics and behaviors. …”
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Real-time algorithmic music composition application.
Published 2022“…In addition, the system also utilises JavaFx and jFugue for its graphical user interface and music programming respectively. …”
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Final Year Project / Dissertation / Thesis -
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Music Recommender System Using Machine Learning Content-Based Filtering Technique
Published 2022“…These are the popular algorithm for unsupervised learning, a machine learning method to analyse and cluster datasets. …”
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Undergraduates Project Papers -
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Learning analytic framework for students’ academic performance and critical learning pathways
Published 2024“…Subsequently, dimensionality reduction techniques like Principal Component Analysis (PCA) and Non-negative matrix factorization (NMF) are applied to capture the most influential course components and grade information. The resulting reduced dataset is then subjected to various clustering algorithms, including partition-based clustering (K-means), hierarchical clustering, and density-based clustering (DBSCAN). …”
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