IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 89- 93

International Journal of Research in Information Technology (IJRIT) www.ijrit.com

ISSN 2001-5569

Review Paper on Artificial Neural Network in Data Mining Ms. Neha Charjan1, Prof. Mahip Bartere2 1

M.E. 2nd SEM, CSE Department, S.G.B.A.U. University Amravati, Maharashtra, India [email protected] 2

M.E. CSE Department, S.G.B.A.U. University Amravati, Maharashtra, India [email protected]

Abstract Artificial neural networks (ANNs) are massively parallel systems with large numbers of interconnected simple processors. This paper describes a computational model to support decision-making using a combination of data mining (DM) and artificial neural network (ANN). With the enormous amount of data stored in databases, files and other repositories, it is increasingly important, to develop a powerful means for analysis and perhaps interpretation data and for the extraction of interesting knowledge that could help in decision-making. Neural Network may have complex structure, long training time, and uneasily understandable representation of results. And often produce incomprehensible models, therefore neural network methods are not commonly used in data mining tasks. Neural networks have high acceptance ability for high accuracy and noisy data and are preferable in data mining. In this paper, introduction of Artificial Neural Network and Data mining is done and the process to achieve the data mining based on neural networks is described. Thus, this paper is an overview of artificial neural. Keywords: Artificial Neural Network (ANNs), Data Mining, Feed forward neural network, Neural Network, Recurrent neural networks, Types of Data Mining Based On Neural Network.

1. Introduction Mining an interdisciplinary subfield of computer science is the process of extracting value from the data. It is also known as knowledge discovery in database and analyses large amount of data which is extracted from large databases such as hidden, previously unknown and potentially useful information. Thus, Data mining is used to extract new knowledge from existing data. The knowledge which extracted using data mining is hidden in data. High quality data, the right data, an adequate sample size and the right tool are required for effective data mining. Data mining classify data into different methods like decision trees, nearest neighbor, neural network. Neural network plays an important role in data mining. Neural network research is motivated by two desires: to obtain better understanding of the human brain and to develop computers that can deal with abstract and poorly defined problems. For example, the conventional computers have trouble for understanding speech and recognizing people's faces. In comparison, human do extremely well at these tasks. There are two types of neural networks: Feed forward neural network and recurrent neural network. Feed forward neural network is simplest type of artificial neural network in which the information moves in Ms. Neha Charjan,



IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 89- 93

only one direction and forward from the input nodes, through the hidden nodes and to the output nodes. There is no loop or cycle in the network and the data processing can extend over multiple (layers of) units, but no feedback connection is present. Whereas in Recurrent neural networks, Contrary to feed forward networks contains feedback connection and models with bi-directional data flow. [1][2]

2. Artificial Neural Network It is one type of network which contains the nodes called as ‘artificial neurons’. These are called artificial neural networks (ANNs). An artificial neuron is a computational model inspired in the natural neurons. ANNs combines artificial neurons to process information. Thus Artificial neural network is defined as mathematical or computation model which based on biological neural networks. ANNs have been successfully applied to broad spectrum of data intensive applications such as finance, medical, industrial, operational analysis, data mining and other. Basically, all the artificial neural networks have similar structure as shown in Fig 1. It consists of at least the three types of layers - input, hidden, and output. The input neurons layer receives data either from input files or from electronic sensors included in real time application. The output layer sends information directly to the outside world, and to secondary computer process, or to other devices such as it is mechanical control system. The number of nodes between the input and output layers are same as the number of attributes and the classes of the problem. Many hidden layers can be between these two layers. The internal layer contains many of the interconnected neurons with various structures. The inputs and outputs of each of these hidden neurons go to other neuron.

Fig.1 Artificial Neural Network [2]

3. Artificial Neural Network in Data Mining The neural network is a non-linear statistical data modelling tool, used to model complex relationships between inputs and outputs or to find patterns in data. By combining two or more artificial neurons we are getting an artificial neural network (ANN). Single Neuron is almost not useful for solving complex real life problems which is achieved by using ANN. By using neural networks as a tool, data warehousing firms are harvesting information from datasets in the process known as data mining. ANN in data mining has various applications such as identify fraud detection in tax and credit card. Forecasting is another application by which prediction of future data on the base of historic data. It can also predict the nature of employees in the firm. Thus, ANN is quite promising for offering solutions to the problems, where traditional models have failed and are very complicated to build. ANNs can be used for many tasks like classification of data, function approximation, data processing, clustering, compression, filtering, regulations, decision making, etc. The neural networks have ability for high acceptance of noisy data and high accuracy and are preferable in data mining. The data mining based on neural network is composed of three phases such as data preparation, rules extracting and rules assessment. The whole process is shown in Fig 2.

Ms. Neha Charjan,



IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 89- 93

Fig. 2 Data mining process based on neural network [3] 3.1 Data Preparation Data preparation is the first important step in the data mining and plays an important role in data mining process. It is used to define and process the mining data to make it fit in specific data mining method. Data mining based on neural networks, as a result of its method’s particularity, the data preparation appears to be especially important, about 50% to 75% development time expenditure in data processing. It includes 3 processes: i) Data Clustering: Clustering is the unsupervised classification of patterns (data item,) into groups (clusters). Clustering eliminate the noise data and correct the inconsistent data. ii) Data Option: Data option is used to select the data to arrange in row. iii) Data Pre-processing: Data pre-processing is to enhance the process of clean data which has been selected. iv) Data Expression: Data expression is to transform the data into the form which can be accepted by neural network based algorithm of the data mining. The data mining based on neural network can only handle by numerical data, so it is needed to transform the sign data into numerical data. [4] 3.2 Rules Extraction There are many methods for extracting rules, in which the most commonly used methods are black-box method, extracting fuzzy rules, link rule, extracting rules from recursive network, the algorithm of binary input, LRE method, output rules extracting, partial rules extracting algorithm and full rules extracting algorithm. 3.3 Rules Assessment The rules can be assessed with the following objectives. Find the optimal sequence of extracting rules; Test the accuracy of rules extracted; detect how much knowledge in the neural network has not been extracted; detects the inconsistency between the extracted rules and the trained neural network. [5]

4. Types of Data Mining Based On Neural Network Mining are hundreds, but there are only two types mostly used are the data mining based on the self-organization neural network and on the fuzzy neural network. 4.1 Data Mining Based on Self-Organization Neural Network Self-organization process is a process of learning without teachers. Through the study, the important characteristics or some inherent knowledge in a group of data, such as the characteristics of the distribution or clustering according to certain feature. In this kind of learning mode the input signal be mapped to the low-dimensional space, and maintain that input signal with same characteristics that corresponding to regional region in space, which is the so-called self-organization feature map.[6] 4.2 Data Mining Based on Fuzzy Neural Network Neural network has functions of learning, Classification, association and memory. But in use of the neural network in data mining, the greatest difficulty is that the output result cannot be intuitively illuminated. After the use of the fuzzy processing function in the neural network, it not only increases its Ms. Neha Charjan,



IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 89- 93

output expression capacity but also the system becomes more stable. Thus, Fuzzy neural network technique can be used as a bridge between numerical and symbolic data representation. Since fuzzy logic has an affinity with the human knowledge representation, it should become a key component of data mining system. By using fuzzy logic we can express Knowledge about a database in a manner which is natural for people to comprehend. [6][7]

5. Application of Artificial Neural Network in data mining: There are many applications of ANN in Data mining; some of these are as follows [8]: 5.1 Accounting i) Identify fraud in tax ii) Enhancing auditing by finding irregularities 5.2 Forecasting i) Predict future data based on historic data. ii) Used in foreign exchange, loan approval, stock market, and change in economics. iii) Predict the nature of employees in the firm. 5.3 Finance i) Detects the bankrupt person ii) Credit card fraud detection iii) Bank note and signature verification iv) Management of risk v) Bond rating and trading 5.4 Marketing i) Predicting employee’s performance and behaviour ii) Analysis of new products iii) Determining personal resources required iv) Sales forecasts

6. Conclusion ANN in data mining plays vital role for classification of the complex data. It helps to generate supervised learning. The good characteristics such as better robustness, own organizing, parallel processing, distributed storages and high degree of fault tolerance makes Data mining a new and important area of research, and neural network is very much suitable for problems solving of data mining. Thus, combination of data mining method and neural network model can greatly improve the efficiency of data mining methods. REFERENCES [1] K Raja Sekhar, V Srinivasa Kalyan, B Phanindra Kumar,”Training Of Artificial Neural Networks in Data Mining”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-2, July 2013. [2] M. Charles Arockiaraj,”Applications of Neural Networks In Data Mining”, Research Inventy: International Journal Of Engineering And Science Vol.3, Issue 1 (May 2013), PP 08-11 Issn (e): 22784721, Issn (p):2319-6483, www.Researchinventy.Com. [3] K. Amarendra, K.V. Lakshmi & K.V. Ramani., “Research on Data Mining Using Neural Networks”, Special Issue of International Journal of Computer Science & Informatics (IJCSI), ISSN (PRINT) : 2231– 5292, Vol.- II, Issue-1, 2 [4] Song Qinbao, Shen Junyi. “The Research of Data Preparation for Data Mining with Neural Networks”, Computer Engineering and Applications 2000; 12:102–104. [5]Agrawal, R., Imielinski, T., Swami, A., “Database Mining: A Performance Perspective”, IEEE Transactions on Knowledge and Data Engineering, pp. 914-925, December 1993 Ms. Neha Charjan,



IJRIT International Journal of Research in Information Technology, Volume 2, Issue 4, April 2014, Pg: 89- 93

[6] Guoquan Jiang, Cuijun Zhao, “The Research of Data Mining Based on Neural Networks”, 2011 International Conference on Computer Science and Information Technology (ICCSIT 2011) IPCSIT vol. 51 (2012) © (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V51.09 [7] Xianjun Ni,”Research of Data Mining Based on Neural Networks”, World Academy of Science, Engineering and Technology 39 2008 [8] H Lu, R Setiono, H Liu,” Effective Data Mining Using Neural Network”, IEEE Transactions on Knowledge and Data Engineering, 1996, 8(6):957-961.

Ms. Neha Charjan,



Review Paper on Artificial Neural Network in Data ...

networks have high acceptance ability for high accuracy and noisy data and are preferable ... applications such as identify fraud detection in tax and credit card.

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