A Study on Conceptual Framework of Data Mining

People have been collecting and organizing data from stone ages. In the earlier days data were collected and recorded in one way or the other mainly for record keeping purposes. With the advancement in computational technology in general and storage technology in particular data collection and their storage in large data warehouses have become an integral part of the data processing and decision-making environment of today’s organizations. Over time people have learned to value data as an important asset. Reliable data in a database or a data warehouse could be used for decision-making purposes by appropriately analyzing the data and making them more meaningful and useful. In other words data could be analyzed to find hidden patterns and foresee trends. The process is broadly being called data mining. Data mining is a process of extraction of useful information and patterns from huge data. It is also called as knowledge discovery process, knowledge mining from data, knowledge extraction or data /pattern analysis.

Data mining is a natural development of the increased use of computerized databases to store data and provide answers to business analysts.

KNOWLEDGE DISCOVERY PROCESS

data. The goal of this technique is to find patterns that were previously unknown. Once these patterns are found they can further be used to make certain decisions for development of their businesses. Three steps involved are

• Exploration

• Pattern identification

• Deployment

Exploration: In the first step of data exploration data is cleaned and transformed into another form, and important variables and then nature of data based on the problem are determined. Pattern Identification: Once data is explored, refined and defined for the specific variables the second step is to form pattern identification. Identify and choose the patterns which make the best prediction. Deployment: Patterns are deployed for desired outcome.

TECHNIQUES

Various algorithms and techniques like Classification, Clustering, Regression, Artificial Intelligence, Neural Networks, Association Rules, Decision Trees, Genetic Algorithm, Nearest Neighbor method etc., are used for knowledge discovery from databases. 1. Classification Classification is the most commonly applied data mining technique, which employs a set of pre- classified examples to develop a model that can classify the population of records at large. Fraud detection and credit risk applications are particularly well suited to this type of analysis. This approach frequently employs decision tree or neural network-based classification algorithms. The data classification process involves learning and classification. In Learning the training data are analyzed by classification algorithm. In classification test data are used to estimate the accuracy of the classification rules. If the accuracy is acceptable the rules can be applied to the new data tuples. For a fraud detection application, this would include complete records of both fraudulent and valid activities determined on a record-by-record basis. The classifier-training algorithm uses these pre-classified examples to determine the set of parameters required for proper discrimination. The algorithm then encodes these parameters into a model called a classifier. Classification by decision tree induction Bayesian Classification Neural Networks Support Vector Machines (SVM) Classification Based on Associations 2. Clustering Clustering can be said as identification of similar classes of objects. By using clustering techniques we can further identify dense and sparse regions in object space and can discover overall distribution pattern and correlations among data attributes. Classification approach can also be used for effective means of distinguishing groups or classes of object but it becomes costly so clustering can be used as preprocessing approach for attribute subset selection and classification. For example, to form group of customers based on purchasing patterns, to categories genes with similar functionality.

• Partitioning Methods

• Hierarchical Agglomerative (divisive) methods

• Density based methods

• Grid-based methods

• Model-based methods

3. Predication Regression technique can be adapted for predication. Regression analysis can be used to model the relationship between one or more independent variables and dependent variables. In data mining independent variables are attributes already known and response variables are what we want to predict. Unfortunately, many real-world problems are not simply prediction. For instance, sales volumes, stock prices, and product failure rates are all very difficult to predict because they may depend on complex interactions of multiple predictor variables. Therefore, more complex techniques (e.g., logistic regression, decision trees, or neural nets) may be necessary to forecast future values. The same model types can often be used for both regression and classification. For example, the CART (Classification and Regression Trees) decision tree algorithm can be used to build both classification trees (to classify categorical response variables) and regression trees (to forecast continuous response variables). Neural

Types of regression methods

• Linear Regression

• Multivariate Linear Regression

• Nonlinear Regression

• Multivariate Nonlinear Regression

4. Association rule Association and correlation is usually to find frequent item set findings among large data sets. This type of finding helps businesses to make certain decisions, such as catalogue design, cross marketing and customer shopping behavior analysis. Association Rule algorithms need to be able to generate rules with confidence values less than one. However the number of possible Association Rules for a given dataset is generally very large and a high proportion of the rules are usually of little (if any) value.

5. Neural networks Neural network is a set of connected input/output units and each connection has a weight present with it. During the learning phase, network learns by adjusting weights so as to be able to predict the correct class labels of the input tuples. Neural networks have the remarkable ability to derive meaning from complicated or imprecise data and can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. These are well suited for continuous valued inputs and outputs. For example handwritten character reorganization, for training a computer to pronounce English text and many real world business problems and have already been successfully applied in many industries. Neural networks are best at identifying patterns or trends in data and well suited for prediction or forecasting needs.

Data mining is a relatively new technology that has not fully matured. Despite this, there are a number of industries that are already using it on a regular basis. Some of these organizations include retail stores, hospitals, banks, and insurance companies. Many of these organizations are combining data mining with such things as statistics, pattern recognition, and other important tools. Data mining can be used to find patterns and connections that would otherwise be difficult to find. This technology is popular with many businesses because it allows them to learn more about their customers and make smart marketing decisions.

Data mining has importance regarding finding the patterns, forecasting, and discovery of knowledge etc., in different business domains. Data mining techniques and algorithms such as classification, clustering etc., helps in finding the patterns to decide upon the future trends in businesses to grow. Data mining has wide application domain almost in every industry where the data is generated that’s why data mining is considered one of the most important frontiers in database and information systems and one of the most promising interdisciplinary developments in Information Technology.

1. Clifton, Christopher (2010). "Encyclopedia Britannica: Definition of Data Mining". Retrieved 2010-12-09. 2. Crisp-DM 1.0 Step by step Data Mining guide from http://www.crisp-dm.org/CRISPWP- 0800.pdf. 3. Dr. Gary Parker, vol 7, 2004, Data Mining: Modules in emerging fields, CD-ROM. 4. Fayyad, U.M., Piatetsky-Shapiro, G. and Smyth, P. Advances in knowledge discovery and data mining. Data Mining to Knowledge Discovery: An Overview, 1-34, AAAI/MIT Press, 1996. 5. Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07. published by Morgan Kauffman, 2nd ed. 7. Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0-471-22852-4. OCLC 50055336 8. Sowa, J.F. Conceptual Structures, Information Processing in Mind and Machine, Addison-Wesley, Reading, Massachusetts, 1984.