Neural network learning improvement using K-means clustering algorithm to improve the performance of web traffic mining

Shrivastava, Vinita; Khan, Mohdilyas; Chaudhari, Vijay K

doi:10.1109/icectech.2011.5941564

Cited by 3 publications

(2 citation statements)

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“…If grouping or clustering partitions a dataset into p sub-datasets, the expected GPC complexity per thread is O((n/p) 3 ), which lowers the overall complexity by p 3 times with p parallel threads. G-KM-MLP, in the same way, reduces BP training complexity from O(n 2 ) [18] to O((n/p) 2 ) per thread. Experiments use computers of 2.7 GHz Intel Core i5, 4GB 1333MHz DDR3 memory, and 4 cores.…”

Section: Speedup Analysismentioning

confidence: 99%

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An Integrated Model for Financial Data Mining

Fan

Le-Khac

Kechadi

2012

Lecture Notes in Computer Science

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Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, churn analysis, real estate analysis, etc. Financial institutes have applied different data mining techniques to enhance their business performance. However, simple approach of these techniques could raise a performance issue. Besides, there are very few general models for both understanding and forecasting different financial fields. We present in this paper a new classification model for analyzing financial data. We also evaluate this model with different realworld data to show its performance.

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Section: Speedup Analysismentioning

confidence: 99%

“…Neural Networks. Multi-layer perceptron (MLP) can handle complex classification problem [18] [19]. However the cons of MLP are clear: No prior idea of the optimal size of hidden layer.…”

Section: Data Mining Techniquesmentioning

confidence: 99%