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Browsing by Author "Marwala, T"

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PAM-lite: Fast and accurate k-medoids clustering for massive datasets

Author: Olukanmi, PO ; Nelwamondo, Fulufhelo V ; Marwala, T Date: Jan 2019 The Partitioning Around Medoids (PAM) clustering algorithm is well-known for its robustness and accuracy, but it is computationally expensive. This paper proposes a fast and accurate version, named PAM-lite. Like CLARA which also addresses ... Read more
Partial imputation to improve predictive modelling in insurance risk classification using a hybrid positive selection algorithm and correlation-based feature selection

Author: Duma, M ; Twala, B ; Nelwamondo, Fulufhelo V ; Marwala, T Date: Sep 2013 We propose a hybrid missing data imputation technique using positive selection and correlation-based feature selection for insurance data. The hybrid is used to help supervised learning methods improve their classification accuracy and ... Read more
Performance evaluation of sampling-based large-scale clustering algorithms

Author: Olukanmi, PO ; Nelwamondo, Fulufhelo V ; Marwala, T Date: Jan 2019 Using benchmark datasets, we study the performances of three efficient clustering algorithms which find cluster centers using a fixed number of random samples. The algorithms are also compared with two other (well-known) algorithms, namely ... Read more
Rethinking k-means clustering in the age of massive datasets: A constant-time approach

Author: Olukanmi, P ; Nelwamondo, Fulufhelo V ; Marwala, T Date: Dec 2019 We introduce a highly efficient k-means clustering approach. We show that the classical central limit theorem addresses a special case (k=1) of the k-means problem and then extend it to the general case. Instead of using the full dataset, our ... Read more
Robust iris segmentation through parameterization of the Chan-Vese algorithm

Author: Mabuza-Hocquet, Gugulethu P ; Nelwamondo, Fulufhelo V ; Marwala, T Date: Jun 2015 The performance of an iris recognition system relies on automated processes from the segmentation stage to the matching stage. Each stage has traditional algorithms used successfully over the years. The drawback is that these algorithms assume ... Read more
Water demand prediction using artificial neural networks and support vector regression

Author: Msiza, IS ; Nelwamondo, Fulufhelo V ; Marwala, T Date: Nov 2008 Computational Intelligence techniques have been proposed as an efficient tool for modeling and forecasting in recent years and in various applications. Water is a basic need and as a result, water supply entities have the responsibility to ... Read more

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Browsing by Author "Marwala, T"

Browsing by Author "Marwala, T"

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