Hybrid Data Mining for Medical Applications

2009 ◽  
pp. 523-543
Author(s):  
Syed Zahid Hassan ◽  
Brijesh Verma
Keyword(s):  
Data Mining ◽  
Medical Applications ◽  
Medical Database ◽  
Data Repositories ◽  
Data Mining Algorithms ◽  
Hybrid Approaches ◽  
Data Mining Approach ◽  
Hybrid Data ◽  
Mining Algorithms ◽  
Existing Data

This chapter focuses on hybrid data mining algorithms and their use in medical applications. It reviews existing data mining algorithms and presents a novel hybrid data mining approach, which takes advantage of intelligent and statistical modeling of data mining algorithms to extract meaningful patterns from medical data repositories. Various hybrid combinations of data mining algorithms are formulated and tested on a benchmark medical database. The chapter includes the experimental results with existing and new hybrid approaches to demonstrate the superiority of hybrid data mining algorithms over standard algorithms.

A Data Mining Approach for Generation of Control Signatures

2002 ◽  
Vol 124 (4) ◽  
pp. 923-926 ◽  
Author(s):  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Metal Forming ◽  
Rough Set Theory ◽  
Decision Rules ◽  
Forming Process ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Metal Forming Process ◽  
Material Forming ◽  
Mining Algorithms

Data mining offers methodologies and tools for data analysis, discovery of new knowledge, and autonomous process control. This paper introduces basic data mining algorithms. An approach based on rough set theory is used to derive associations among control parameters and the product quality in the form of decision rules. The model presented in the paper produces control signatures leading to good quality products of a metal forming process. The computational results reported in the paper indicate that data mining opens a new avenue for decision-making in material forming industry.

Fault Monitoring of Wind Turbine Generator Brushes: A Data-Mining Approach

2012 ◽  
Vol 134 (2) ◽  
Author(s):  
Anoop Verma ◽  
Andrew Kusiak
Keyword(s):  
Data Mining ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Turbine Generator ◽  
Wind Turbine Generator ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Carbon Brushes ◽  
Critical Components ◽  
Mining Algorithms

Components of wind turbines are subjected to asymmetric loads caused by variable wind conditions. Carbon brushes are critical components of the wind turbine generator. Adequately maintaining and detecting abnormalities in the carbon brushes early is essential for proper turbine performance. In this paper, data-mining algorithms are applied for early prediction of carbon brush faults. Predicting generator brush faults early enables timely maintenance or replacement of brushes. The results discussed in this paper are based on analyzing generator brush faults that occurred on 27 wind turbines. The datasets used to analyze faults were collected from the supervisory control and data acquisition (SCADA) systems installed at the wind turbines. Twenty-four data-mining models are constructed to predict faults up to 12 h before the actual fault occurs. To increase the prediction accuracy of the models discussed, a data balancing approach is used. Four data-mining algorithms were studied to evaluate the quality of the models for predicting generator brush faults. Among the selected data-mining algorithms, the boosting tree algorithm provided the best prediction results. Research limitations attributed to the available datasets are discussed.

Data Mining Algorithms, Fog Computing

2018 ◽  
pp. 231-264
Author(s):  
S. Thilagamani ◽  
A. Jayanthiladevi ◽  
N. Arunkumar
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Fog Computing ◽  
Data Warehouses ◽  
Data Repositories ◽  
Data Mining Algorithms ◽  
Classification Prediction ◽  
Mining Algorithms

Different methods are used to mine the large amount of data presents in databases, data warehouses, and data repositories. The methods used for mining include clustering, classification, prediction, regression, and association rule. This chapter explores data mining algorithms and fog computing.

scholarly journals Improving daily stochastic streamflow prediction: Comparison of novel hybrid data mining algorithms

2021 ◽  
Author(s):  
Khabat Khosravi ◽  
Ali Golkarian ◽  
Martijn J. Booij ◽  
Rahim Barzegar ◽  
Wei Sun ◽  
...  
Keyword(s):  
Data Mining ◽  
Streamflow Prediction ◽  
Data Mining Algorithms ◽  
Hybrid Data ◽  
Mining Algorithms

scholarly journals Prediction for rice yield using data mining approach in Ranga Reddy district of Telangana, India

2021 ◽  
Vol 23 (2) ◽  
pp. 242-248
Author(s):  
BABY AKULA ◽  
R.S.PARMAR ◽  
M. P. RAJ ◽  
K. INDUDHAR REDDY
Keyword(s):  
Data Mining ◽  
Logistic Model ◽  
Yield Data ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Rice Yields ◽  
Using Data ◽  
Mlp Classifier ◽  
Mining Algorithms ◽  
Best Fit

In order to explore the possibility of crop estimation, data mining approach being multidisciplinary was followed. The district of Ranga Reddy, Telangana State, India has been chosen for the study and its year wise average yield data of rice and daily weather over a period of 31 years i.e. from 1988-2019 (30th to 47th Standard Meteorological Weeks). Data mining tool WEKA (V3.8.1). Min- Max Normalization technique followed by Feature Selection algorithm, ‘cfsSubsetEval’ was also adopted to improve quality and accuracy of data mining algorithms. Thus, after cleaning and sorting of data, five classifiers viz., Logistic, MLP (Multi Layer Perceptron), J48 Classifier, LMT (Logistic Model Trees) and PART Classifier were employed over the trained data. The results indicated that the function based and tree based models have better performance over rule based model. In case of function based two models examined, viz., Logistic and MLP, the later performed better over Logistic model. Between tree based two models, LMT performed better over J48. Thus, MLP classifier model found to be the best fit model in predicting rice yields as it recorded an accuracy of 74.19 %, sensitivity of 0.742 and precision of 0.743 as compared with other models. The MLP has also achieved the highest F1 score of (0.742) and MCC (0.581).

scholarly journals A Bitwise Design and Implementation for Privacy-Preserving Data Mining: From Atomic Operations to Advanced Algorithms

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Baek Kyung Song ◽  
Joon Soo Yoo ◽  
Miyeon Hong ◽  
Ji Won Yoon
Keyword(s):  
Data Mining ◽  
Fundamental Problem ◽  
Homomorphic Encryption ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Encrypted Data ◽  
Design And Implementation ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Existing Data

Homomorphic encryption (HE) is considered as one of the most powerful solutions to securely protect clients’ data from malicious users and even severs in the cloud computing. However, though it is known that HE can protect the data in theory, it has not been well utilized because many operations of HE are too slow, especially multiplication. In addition, existing data mining research studies using encrypted data focus on implementing only specific algorithms without addressing the fundamental problem of HE. In this paper, we propose a fundamental design and implementation of data mining algorithm through logical gates. In order to do this, we design various logic of atomic operations in encrypted domain and finally apply these logic to well-known data mining algorithms. We also analyze the execution time of atomic and advanced algorithms.

scholarly journals Benchmarking data mining approaches for traveler segmentation

2021 ◽  
Vol 11 (1) ◽  
pp. 409
Author(s):  
Tamer Uçar ◽  
Adem Karahoca
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Machine Learning Algorithms ◽  
Travel Agency ◽  
Data Set ◽  
Data Mining Algorithms ◽  
Travel Agencies ◽  
User Data ◽  
Hybrid Data ◽  
Mining Algorithms

The purpose of this study is proposing a hybrid data mining solution for traveler segmentation in tourism domain which can be used for planning user-oriented trips, arranging travel campaigns or similar services. Data set used in this work have been provided by a travel agency which contains flight and hotel bookings of travelers. Initially, the data set was prepared for running data mining algorithms. Then, various machine learning algorithms were benchmarked for performing accurate traveler segmentation and prediction tasks. Fuzzy C-means and X-means algorithms were applied for clustering user data. J48 and multilayer perceptron (MLP) algorithms were applied for classifying instances based on segmented user data. According to the findings of this study, J48 has the most effective classification results when applied on the data set which is clustered with X-means algorithm. The proposed hybrid data mining solution can be used by travel agencies to plan trip campaigns for similar travelers.

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