Oct 30, PDF | The objective of this paper is to present a review literature on what are impacts of Data Mining (DM) in Business Intelligence (BI). Master's Degree. Data Mining and. Business Intelligence. Knowledge Branch. Social and Legal Sciences. Responsible Center. Faculty of Statistical Studies. Data Mining and Business Intelligence. Lecture 1/DMBI/IKIT/MTI/UI. Yudho Giri Sucahyo, Ph.D, CISA ([email protected]). Faculty of Computer Science.
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Nov 29, soundofheaven.info Library of Congress Cataloging-in-Publication Data: Shmueli, Galit, Data mining for business intelligence: concepts. A primer on data modeling is included for those uninitiated in this topic. Keywords . Data Analytics, Data Mining, Business Intelligence, Decision Trees. DATA MINING. FOR BUSINESS ANALYTICS. Concepts, Techniques, and Applications in R. Galit Shmueli. Peter C. Bruce. Inbal Yahav. Nitin R. Patel. Kenneth.
These data sets could come from a single database or could come from integrated data established in a data warehouse. Business analytics is the use of analysis techniques and decision rules to provide business users with critical insights of the operational and performance characteristics in every aspects of the business. The distinction between data mining, business analytics and business intelligence is presented. The dependent variable is normally categorical. Data Mining. If the assessment of the model concluded that it is insignificant, the data mining exercise can be repeated using a bigger sample data set or alternatively using new data attributes. In an online purchase scenario, credit card details will be entered in the website.
The latter is termed as knowledge discovery , a non-trivial process to identify valid, novel, practical and recognizable patterns in data residing in huge databases. The terms knowledge discovery and data mining are sometimes used interchangeably.
However, the formats in which these data are represented may not all be suitable for data mining purpose. Thus, the first phase of data mining strategy is to prepare the data.
Data preparation is a critical activity and is often the most time consuming activity in data mining. It involves converting unsuitable data formats into specific formats that lend themselves readily for data mining. Once data preparation is completed, the second phase involves selecting appropriate data mining and text mining techniques to search for patterns in the data sets.
Each approach invokes a particular algorithm that will systematically search for specific forms of pattern in the data sets. If the assessment of the model concluded that it is insignificant, the data mining exercise can be repeated using a bigger sample data set or alternatively using new data attributes.
Figure 1: For example, if a grocery chain develops an association model that shows there is a high percentage of people who purchase baby diapers on weekends also purchase sports magazines, the chain could deploy a business plan that make sports magazines an obvious display item next to diapers. This is a case of knowledge discovery followed by relevant decision making. This is especially so for e-businesses.
This phenomenon is primarily driven by the abundance of data created by advances in information and communication technologies as well as the increasing use of the Internet to conduct business operations. The data mining technique related to association is commonly applied in the retail industry.
The main application here is to associate a basket of products that are most likely to be purchased by customers. This application is commonly called market basket analysis.
Results from market basket analysis help retailers to cross sell products, develop focused promotions and design more effective shelf arrangement of products. A good example is the online book or music retailers whereby the moment you purchase or select a particular item, the online retailer will suggest various other items of similar genre to you. On websites, e-businesses can also place their products that have high association on the same webpage to entice online customers to increase their purchase volume.
Association rules are usually derived using apriori algorithm  that detects important relationships among cross-tabulation tables. Clustering, as a data mining technique, is commonly used by businesses to perform customer segmentation. Customer segmentation is usually used to support marketing promotions and target selling.
Such an application of clustering is termed risk management. Clustering can also be applied to detect fraud. Such applications could be found in the credit card industry whereby data mining models are built to detect possible fraudulent credit card transactions. One of the most common techniques used in clustering is the K-Means algorithm , using Euclidean distance to identify distinct clusters in data.
For e-businesses, the ability to have real-time identification of customer segments, credit risks, or fraudulent transactions is critical for business competitiveness and to mitigate business risks. Figure 2 provides an illustration of how data mining can be deployed to detect fraudulent credit card transactions in e-businesses. In an online purchase scenario, credit card details will be entered in the website.
The information will then be transmitted via Internet to a processing center for verification, authentication and approval. It is at the processing center whereby fraud detection models derived from data mining clustering can be applied to evaluate the possibility of fraudulent cards.
With a quick assessment by the model, the card payment is either placed in the low probability or high probability categories and automated decision can be initiated as to whether final approval will be given to accept or reject card payment. Figure 2: Application of Data Mining to Detection of Fraudulent Credit Card Transactions Besides clustering and association, the other commonly used data mining approach in business applications is classification, estimation or prediction. Classification, estimation or prediction is based on induction-based supervised learning.
In classification, the goal is to place records into defined classes. The dependent variable is normally categorical. In estimation, the goal is similar to classification.
However, the dependent variable is normally numerical. Both methods attempt to predict in which defined class should a new instance be placed an instance is a record in a data set.
In prediction, the goal is to determine a future outcome rather than to place new instances in pre-defined classes. Although classification, estimation or prediction can be used in many applications, one of the most common applications is to build churn models. In industry, churn modeling evaluates which customers are likely to leave in the near future. Decision tree , statistical regression and neural network are the common techniques employed in this data mining approach.
Churn models are important in e-businesses. If the likelihood is high in the near future, proactive action could be initiated to prevent customer from switching to competitors. Such proactive actions include offering discounts, lower pricing, and other enticing propositions to the customer. Businesses are beginning to realize that the application of data mining and text mining provides them with a competitive edge.
With data increasing in an exponential manner, the ability to use data mining to sieve through massive amount of data and identify relevant patterns will become a strategic tool in improving key areas of the business such as customers, operations and the supply chain.
Agrawal, R. Koh, H. Lloyd, S. Quinlan, J.
Machine Learning, 1, 1, pp. Usama M. Related Papers. Use of Data Mining in Banking. By Vijay Saini. IJERA www. Data Mining For Marketing. By Publisher ijmra. Intelligence Gathering and Crime Analysis.
Data Mining, a Heuristic Approach. Introduction to data mining and knowledge discovery. Introduction to Data Mining and Knowledge Discovery. Introduction to Data Mining and its Applications. Data Warehousing and Data Mining for Telecommunications.
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