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The neural networks have been used and tried for many intelligent processes to mimic human decision making power. Classification technique is a vital step in data mining for intelligent applications. Neural networks are quite often trained much in similar way of human learning process. The efficacy of neural networks depends upon how precisely these are trained. Training can be of many types. We have chosen harmony search algorithm to train the neural networks for its known advantages. The scope of this paper is focused on to examine the supervised learning of neural networks by employing harmony search algorithm to predict market selling trends for different items in order to support decision making and planning. We gathered data sets from market transactions to train and test real time and online neural network model. The model predicts sales automatically and updates the records after every transaction. We have implemented and tested the model over a long period by comparing actual sales with predicted ones and taking cyclical and seasonal effects into account. Results are quite encouraging and have shown good accuracy. We have analysed model by comparing with existing alternative techniques. It shows very competitive and high classification accuracy.
The neural networks have been used and tried for many intelligent processes to mimic human decision making power. Classification technique is a vital step in data mining for intelligent applications. Neural networks are quite often trained much in similar way of human learning process. The efficacy of neural networks depends upon how precisely these are trained. Training can be of many types. We have chosen harmony search algorithm to train the neural networks for its known advantages. The scope of this paper is focused on to examine the supervised learning of neural networks by employing harmony search algorithm to predict market selling trends for different items in order to support decision making and planning. We gathered data sets from market transactions to train and test real time and online neural network model. The model predicts sales automatically and updates the records after every transaction. We have implemented and tested the model over a long period by comparing actual sales with predicted ones and taking cyclical and seasonal effects into account. Results are quite encouraging and have shown good accuracy. We have analysed model by comparing with existing alternative techniques. It shows very competitive and high classification accuracy.
In this era of Internet, there are many lots of retailers in the e-commerce industry for whom the customers are assets. E-commerce means buying and selling the products through Internet in online mode. E-commerce established many employment opportunities to the people from anywhere because there is no direct interaction between the seller and the buyer. Many people are purchasing things using this e-commerce application. There are many e-commerce websites available for the customers. So the Retailers want to analyze their relationship with the customers so that they can produce or buy the goods according to their requirements. However, this work mainly focuses on predicting the customer lifetime value (CLV) using Beta-Geometric/Negative Binomial Distribution Model (BG\NBD) and Gamma-Gamma Model.
This research paper is a comprehensive report on experimental setup, data collection methods, implementation and result analyses of market segmentation and forecasting using neural network based artificial intelligence tools. The main focus of the paper is on visual data mining applications for enhancing business decisions. The software based system is implemented as a fully automated and intelligent enough to take into effect of each sales transaction. It updates and instantly modifies forecasting statistics by receiving input sales data directly from sales counter through networked connectivity. The connectivity may be wired or wireless. Three artificial intelligence tools, namely decision tree, ensemble classifier and Self Organizing Maps (SOM) are used for data processing and data analysis. The visual data mining concept is implemented by presenting results in the form of visual interpretation in as simple as possible way to understand very complex statistics. The current research results are mapped to interactive visualization by using multilevel pie charts, multi bar charts, histograms, scatter plots, tree maps and dataflow diagrams. The different visualization techniques help in understanding different levels of information hidden in very large data sets. The results analysis show that decision tree has classified data correctly up to a 86.0 %, ensemble techniques produced an average of 88.0 % and the predictions using SOM has accuracy of 90.0 %. The survey carried out after implementation and use of the system shows that the system is very easy to understand and can be interpreted quickly with minimum efforts. General TermsComputer science and engineering, information technology, data mining, market, business.
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