Subspace Projection–Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service

Lee, Heon Gyu; Choi, Yong Hoon; Jung, Hoon; Shin, Yong Ho

doi:10.4218/etrij.15.2314.0068

Cited by 4 publications

(2 citation statements)

References 17 publications

(21 reference statements)

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“…We also used Precision, Recall, F-measure and Accuracy to evaluate the classifiers' performance for analyzing our training sets with imbalanced class distribution. Formal definitions of these measures are given in the equations below [15,26,27]:…”

Section: Classification Based On Multiple Association Rules (Cmar)mentioning

confidence: 99%

A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

et al. 2016

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Abstract:In this paper, we proposed not only an extraction methodology of multiple feature vectors from ultrasound images for carotid arteries (CAs) and heart rate variability (HRV) of electrocardiogram signal, but also a suitable and reliable prediction model useful in the diagnosis of cardiovascular disease (CVD). For inventing the multiple feature vectors, we extract a candidate feature vector through image processing and measurement of the thickness of carotid intima-media (IMT). As a complementary way, the linear and/or nonlinear feature vectors are also extracted from HRV, a main index for cardiac disorder. The significance of the multiple feature vectors is tested with several machine learning methods, namely Neural Networks, Support Vector Machine (SVM), Classification based on Multiple Association Rule (CMAR), Decision tree induction and Bayesian classifier. As a result, multiple feature vectors extracted from both CAs and HRV (CA+HRV) showed higher accuracy than the separative feature vectors of CAs and HRV. Furthermore, the SVM and CMAR showed about 89.51% and 89.46%, respectively, in terms of diagnosing accuracy rate after evaluating the diagnosis or prediction methods using the finally chosen multiple feature vectors. Therefore, the multiple feature vectors devised in this paper can be effective diagnostic indicators of CVD. In addition, the feature vector analysis and prediction techniques are expected to be helpful tools in the decisions of cardiologists.

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Section: Classification Based On Multiple Association Rules (Cmar)mentioning

confidence: 99%

A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

et al. 2016

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“…MapReduce processes the data by utilizing map and reduce functions being commonly used in functional programming. Currently, Hadoop [3], one of the most popular MapReduce frameworks, is widely utilized in the various field of real world application [4]- [7].…”

Section: Introductionmentioning

confidence: 99%

Cancelled: A New Efficient Resource Management Framework for Iterative MapReduce Processing in Large-Scale Data Analysis

Hong

Park

Lim

et al. 2017

IEICE Trans. Inf. & Syst.

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Seungtae HONG†a) , Kyongseok PARK † †b) , Chae-Deok LIM †c) , Nonmembers, and Jae-Woo CHANG † † †d) , Member SUMMARYTo analyze large-scale data efficiently, studies on Hadoop, one of the most popular MapReduce frameworks, have been actively done. Meanwhile, most of the large-scale data analysis applications, e.g., data clustering, are required to do the same map and reduce functions repeatedly. However, Hadoop cannot provide an optimal performance for iterative MapReduce jobs because it derives a result by doing one phase of map and reduce functions. To solve the problems, in this paper, we propose a new efficient resource management framework for iterative MapReduce processing in large-scale data analysis. For this, we first design an iterative job state-machine for managing the iterative MapReduce jobs. Secondly, we propose an invariant data caching mechanism for reducing the I/O costs of data accesses. Thirdly, we propose an iterative resource management technique for efficiently managing the resources of a Hadoop cluster. Fourthly, we devise a stop condition check mechanism for preventing unnecessary computation. Finally, we show the performance superiority of the proposed framework by comparing it with the existing frameworks. key words: large-scale data analysis, iterative data processing framework, MapReduce, Hadoop

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Zur zielgrößenabhängigen Bestimmung der (Erfolgs-) Faktoren im Targeting

Lindenbeck

2019

Dialogmarketing Perspektiven 2018/2019

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Subspace Projection–Based Clustering and Temporal ACRs Mining on MapReduce for Direct Marketing Service

Cited by 4 publications

References 17 publications

A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

A Data Mining Approach for Cardiovascular Disease Diagnosis Using Heart Rate Variability and Images of Carotid Arteries

Cancelled: A New Efficient Resource Management Framework for Iterative MapReduce Processing in Large-Scale Data Analysis

Zur zielgrößenabhängigen Bestimmung der (Erfolgs-) Faktoren im Targeting

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