scholarly journals Classification of electricity consumers using artificial neural networks

2019 ◽  
Vol 32 (4) ◽  
pp. 529-538
Author(s):  
Dragana Knezevic ◽  
Marija Blagojevic
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Electricity Consumption ◽  
Test Dataset ◽  
Data Mining Techniques ◽  
Network Output ◽  
Numerous Data ◽  
High Tariff ◽  
Entire Dataset

This paper explains the process of using neural networks, as one of numerous data mining techniques, for the classification of electricity consumers. The processed data comprised more than a million recordings of electricity consumption for 21,643 consumers over the period of four years and eight months. Using a data subset (70% of the entire dataset), the network was trained for the classification of consumers according to the type of the electric meter they possess (single-rate or dual-rate) and the zone they live in (city or village). The network input data in both cases included: consumer code, reading period from-to, current and previous meter reading for both low and high tariff, dual and single rate tariff consumption for that period and their total amount, as independent variables, whereas the network output comprised dependent variable classes (zone or type of electric meter). The results show that a network created in this way can be trained so well that it achieves high precision when evaluated using the test dataset. Using the available recordings about electricity consumption, the type of the electric meter consumers possess and the zone they live in can be predicted with the accuracy of 77% and 82%, respectively. These findings can provide the basis for further research using other data mining techniques.

Classification of jobs with risk of low back disorders by applying data mining techniques

2005 ◽  
Vol 4 (4) ◽  
pp. 291-305
Author(s):  
Jozef Zurada ◽  
Waldemar Karwowski ◽  
William Marras
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Logistic Regression ◽  
Decision Tree ◽  
Low Back ◽  
Data Mining Techniques ◽  
Back Disorders ◽  
Work Related ◽  
Low Back Disorders

Work related low back disorders (LBDs) continue to pose significant occupational health problem that affects the quality of life of the industrial population. The main objective of this study was to explore the application of various data mining techniques, including neural networks, logistic regression, decision trees, memory-based reasoning, and the ensemble model, for classification of industrial jobs with respect to the risk of work-related LBDs. The results from extensive computer simulations using a 10-fold cross validation showed that memory-based reasoning and ensemble models were the best in the overall classification accuracy. The decision tree and memory-based reasoning models were the most accurate in classifying jobs with high risk of LBDs, whereas neural networks and logistic regression were the best in classifying jobs with low risk of LBDs. The decision tree model delivered the most stable results across 10 generations of different data sets randomly chosen for training, validation, and testing. The classification results generated by the decision tree were the easiest to interpret because they were given in the form of simple 'if-then' rules. These results produced by the decision tree method showed that the peak moment had the highest predictive power of LBDs.

Classification of Intrusion Detection Using Data Mining Techniques

2018 ◽  
pp. 753-764 ◽  
Author(s):  
Roma Sahani ◽  
Shatabdinalini ◽  
Chinmayee Rout ◽  
J. Chandrakanta Badajena ◽  
Ajay Kumar Jena ◽  
...  
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Data Mining Techniques ◽  
Using Data

Classification of Product Rating Using Data Mining Techniques

2017 ◽  
pp. 27-36 ◽  
Author(s):  
Pinku Deb Nath ◽  
Sowvik Kanti Das ◽  
Fabiha Nazmi Islam ◽  
Kifayat Tahmid ◽  
Raufir Ahmed Shanto ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Techniques ◽  
Using Data ◽  
Product Rating

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

scholarly journals Application of Data Mining Techniques for Medical Data Classification: A Review

2018 ◽  
Vol 150 ◽  
pp. 06003 ◽  
Author(s):  
Saima Anwar Lashari ◽  
Rosziati Ibrahim ◽  
Norhalina Senan ◽  
N. S. A. M. Taujuddin
Keyword(s):  
Data Mining ◽  
Data Classification ◽  
Past Research ◽  
Medical Data ◽  
Extensive Literature ◽  
Literature Analysis ◽  
Data Mining Techniques ◽  
Medical Data Classification ◽  
Using Data

This paper investigates the existing practices and prospects of medical data classification based on data mining techniques. It highlights major advanced classification approaches used to enhance classification accuracy. Past research has provided literature on medical data classification using data mining techniques. From extensive literature analysis, it is found that data mining techniques are very effective for the task of classification. This paper analysed comparatively the current advancement in the classification of medical data. The findings of the study showed that the existing classification of medical data can be improved further. Nonetheless, there should be more research to ascertain and lessen the ambiguities for classification to gain better precision.

Sign in / Sign up

Export Citation Format

Share Document