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A web-based implementation of k-means algorithms
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Final Year Project / Dissertation / Thesis -
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Big Data Mining Using K-Means and DBSCAN Clustering Techniques
Published 2022“…Results obtained after pre-processing phase showed that the data quality will improve when the number of records reduced by (51.45). The density-based spatial clustering of applications with noise (DBSCAN) and the K-means algorithm were used to develop clustering algorithms. …”
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Article -
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Web based clustering tool using K-MEAN++ algorithm / Muhammad Nur Syazwanie Aznan
Published 2019“…Which is why this project objective is to develop a web based clustering tool using K-MEAN++ algorithm. This project will use the rapid application development (RAD) methodology since this are the most suitable method for developing the system. …”
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Thesis -
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Data mining in network traffic using fuzzy clustering
Published 2003“…In this project, I have developed a program to capture and filter the packets based on the application. The fuzzy clustering process are made using three algorithms : Fuzzy C-Means (FCM), Gustafsof-Kessel (GK) and Gath-Geva (GG) algorithm. …”
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Data mining in network traffic using fuzzy clustering
Published 2003“…In this project, I have developed a program to capture and filter the packets based on the application. The fuzzy clustering process are made using three algorithms : Fuzzy C-Means (FCM), Gustafsof-Kessel (GK) and Gath-Geva (GG) algorithm. …”
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Clustering of rainfall data using k-means algorithm
Published 2019“…K-Means algorithm is used to obtain optimal rainfall clusters. …”
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Conference or Workshop Item -
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Logistic regression methods for classification of imbalanced data sets
Published 2012“…Hence, it is required to develop effective imbalanced LR-based methods to be widely used in data mining applications. …”
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Stock price monitoring system
Published 2024“…Consequently, Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to evaluate the performance of the prediction algorithms. …”
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Final Year Project / Dissertation / Thesis -
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USING LATENT SEMANTIC INDEXING FOR DOCUMENT CLUSTERING
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Thesis -
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A Hybrid Approach For Web Search Result Clustering Based On Genetic Algorithm With K-means
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journal::journal article -
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Mining Sequential Patterns Using I-PrefixSpan
Published 2007“…Sequential pattern mining is a relatively new data-mining problem with many areas of application. …”
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Conference or Workshop Item -
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Simple quantum circuit for pattern recognition based on nearest mean classifier
Published 2016“…Machine learning plays a key role in many applications such as data mining and image recognition. …”
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Article -
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Mining Sequential Patterns using I-PrefixSpan
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Citation Index Journal -
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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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Article -
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Context aware app recommendation using email semantic analysis
Published 2019“…In order to increase the functionalities for the email application and further enhance the user experien ce, it is proposed to develop a recommendation system to work with the email application that provide s a recommendation based on the email content. …”
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Final Year Project / Dissertation / Thesis -
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Partitional clustering algorithms for highly similar and sparseness y-short tandem repeat data / Ali Seman
Published 2013“…For the overall performances which were based on the six data sets, the &-AMH algorithm recorded the highest mean accuracy scores of 0.93 as compared to the other algorithms: the ^-Population (0.91), the &-Modes-RVF (0.81), the New Fuzzy &-Modes (0.80), A:-Modes (0.76), &-Modes-HI (0.76), £-Modes- HII (0.75), Fuzzy £-Modes (0.74) and £-Modes-UAVM (0.70). …”
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Fireflyclust: an automated hierarchical text clustering approach
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Article -
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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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