Titlebook: Inland Waters of Southern Africa: An Ecological Perspective; B. R. Allanson,R. C. Hart,R. D. Robarts Book 1990 Kluwer Academic Publishers

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t data, time-series data, sequence data, graph data, and spatial data. .Application chapters: These chapters study important applications such as stream mining, Web mining, ranking, recommendations, social networks, and privacy preservation. The domain chapters also have an applied flavor. .Appropri

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B. R. Allanson,R. C. Hart,J. H. O’Keeffe,R. D. Robartsave been proposed in this chapter and are used to cope with this difficulty. A heuristic algorithm that consists of two stages is developed. The developed similarity coefficient is used in stage 1 to obtain basic machine cells. Stage 2 solves the machine capacity violated issue, assigns parts to cel

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B. R. Allanson,R. C. Hart,J. H. O’Keeffe,R. D. Robartsld scenarios, there are circumstances where survey data are unavailable or unreliable. In this paper, we present a new customer wallet share estimation approach. In the proposed approach, a predictive model based on decision trees facilitates an accurate estimation of wallet shares for customers rel

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B. R. Allanson,R. C. Hart,J. H. O’Keeffe,R. D. Robartsences in phishing attack features detected for different countries. We have collected a real world Twitter dataset over 6 months and show that we are able to detect phishing successfully using US phishing models despite only a low level of phishing occurring in smaller populations such as New Zealan

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B. R. Allanson,R. C. Hart,J. H. O’Keeffe,R. D. Robartsexperts to identify cohorts that are more relevant to a particular pre-defined purpose. Moreover, the proposed method leverages powerful deep learning-based embedding techniques to incrementally gain effective representations for the complex structures inherit in patient journey data. We experimenta

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B. R. Allanson,R. C. Hart,J. H. O’Keeffe,R. D. Robartsty based on 11 quasi-identifiers, with less than 3% suppression, compared with only 3-anonymity based on no more than 8 quasi-identifiers with far more than 3% suppression commonly reported in literature. Furthermore, our method enabled random forest classifier to achieve 0.996 for AUC and 0.895 for

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