2. DATA MINING IN INFORMATION MANAGEMENT – APPROACHES AND IMPLICATIONS BY 1.OSHODI ISMAIL OLAKUNBI [20130204134] 2.SALAKO TESLIM AKOLADE [20130204149] 3.ODUTAYO OLADIPUPO BIODUN [20130204105] SUPERVISOR-IN-CHARGE: MR. ODULAJA G.O.

3. Introduction To Data Mining Data mining which is also known as knowledge discovery is the process in which we extract useful information from large set of data. It is also the extraction of hidden predictive information from large databases, it is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside

4. their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?“. This project focuses on the approaches/techniques of data mining and the implications of data mining. But briefly we would like to discuss the uses of data mining.

5. Uses of Data Mining AI/Machine Learning Combinatorial/Game Data Mining Good for analyzing winning strategies to games, and thus developing intelligent AI opponents. (ie: Chess) Business Strategies Market Basket Analysis Identify customer demographics, preferences, and purchasing patterns. Risk Analysis Product Defect Analysis Analyze product defect rates for given plants and predict possible complications (read: lawsuits) down the line.

6. Uses of Data Mining (Continued) User Behavior Validation Fraud Detection In the realm of cell phones Comparing phone activity to calling records. Can help detect calls made on cloned phones. Similarly, with credit cards, comparing purchases with historical purchases. Can detect activity with stolen cards.

7. Data Mining Approaches Several core approaches that are used in data mining describe the type of mining and data recovery operation. Let's look at some key approaches and examples. Association Approach Association (or relation) is probably the better known and most familiar and straightforward data mining approach. Here, you make a simple correlation between two or more items, often of the same type to identify patterns. For example, when tracking people's buying habits, you might identify that a customer always buy cream when they buy strawberries, and therefore suggest that the next time that they buy strawberries they might also want to buy cream. With the help of association rule, market analyst analyze the customer behavior accordingly to see their buying pattern. I would like to give a real time example, if you were visiting an online shopping website to shop for mobile phones then they start to give you suggestion you may also like this item(e.g. powerbank, selfie sticks e.t.c.). It means they are analyzing their customers shopping pattern and this is done through the association rule.

8. Data Mining Approaches (Continued) Clustering Approach By examining one or more attributes or classes, you can group individual pieces of data together to form a structure opinion. At a simple level, clustering is using one or more attributes as your basis for identifying a cluster of correlating results. Clustering is useful to identify different information because it correlates with other examples so you can see where the similarities and ranges agree. Clustering can work both ways. You can assume that there is a cluster at a certain point and then use our identification criteria to see if you are correct. The image in Figure 3 shows a good example.

9. Data Mining Approaches (Continued) Figure 3. Clustering In the example, we can identify three colors, which are separated into 3 groups accordingly to their color similarity.

10. Data Mining Approaches (Continued) Classification Approach You can use classification to build up an idea of the type of customer, item, or object by describing multiple attributes to identify a particular class. For example, you can easily classify cars into different types (sedan, 4x4, convertible) by identifying different attributes (number of seats, car shape, driven wheels). Given a new car, you might apply it into a particular class by comparing the attributes with our known definition. You can apply the same principles to customers, for example by classifying them by age and social group. For instance we created an algorithm that classified all the phone numbers on TASUED PORTAL to their various classes( networks i.e. MTN,AIRTEL,GLO,ETISALAT). With this we were able to gain knowledge about the most used network in TASUED, by mining the already structured data we got from TASUED PORTAL, we were able to deduce that 61% of Tasuedites Use MTN 19% of Tasuedites Use ETISALAT 10% of Tasuedites Use GLO 10% of Tasuedites also use AIRTEL

11. Data Mining Approaches (Continued) In a scenario where TASUED is considering which bulk sms service to use and are provided with this 2 options: i. The first bulk sms service charges 2naira per sms sent to MTN and ETISALAT network users and 5naira per sms to other networks users (GLO and AIRTEL). ii. The second bulk sms service charges 2naira per sms sent to GLO and AIRTEL network users and 5naira per sms to other network users (MTN and ETISALAT). With the above mined data it will help TASUED to decide better on which of the two bulk sms service they are going to use, which will be the first option since they have more users using MTN and ETISALAT networks. Prediction Prediction is a wide topic and runs from predicting the failure of components or machinery, to identifying fraud and even the prediction of company profits. Used in combination with the other data mining techniques, prediction involves analyzing trends, classification, pattern matching, and relation. By analyzing past events or instances, you can make a prediction about an event. Using the credit card authorization, for example, you might combine decision tree analysis of individual past transactions with classification and historical pattern matches to identify whether a transaction is fraudulent. Making a match between the purchase of flights to the US and transactions in the US, it is likely that the transaction is valid.

12. Data Mining Approaches (Continued) Decision trees Related to most of the other techniques (primarily classification and prediction), the decision tree can be used either as a part of the selection criteria, or to support the use and selection of specific data within the overall structure. Within the decision tree, you start with a simple question that has two (or sometimes more) answers. Each answer leads to a further question to help classify or identify the data so that it can be categorized, or so that a prediction can be made based on each answer.

13. Data Mining Techniques (Continued) In above diagram there are three types of people; young, middle age and senior and they can decide to buy a laptop or not depending on some situation and the leaf nodes are showing the result, whether a person will buy a laptop or not.

14. Data Mining Implications It should be clear that data mining itself is not ethically problematic. The ethical dilemmas arise when mining is executed over data of a personal nature. For example, mining manufacturing data is unlikely to lead to any consequences of a personally objectionable nature. However, mining a clickstream of data obtained from an oblivious Internet user instigates a variety of ethical problems. Perhaps the most immediately apparent of these is the invasion of privacy. Privacy The concerns about personal privacy have been increasing enormously recently especially when the internet is booming with social networks, e- commerce, forums. Because of privacy issues, people are afraid of their personal information been collected and used in an unethical way potentially causing them a lot of troubles. Solution: One solution to the invasion of privacy problem is the anonymisation of personal data (Clarke 1997). This has the effect of providing some level of privacy protection for data subjects. A suggested compromise is the empowerment of individuals to dictate the amount and type of personal data they consider appropriate for an organisation to mine.

15. Data Mining Implications (Continued) Data Accuracy When mining is executed over expired data inaccurate patterns are more likely to be revealed, which can lead to negative consequences for an individual specifically and groups and society in general. Likewise, there is a great likelihood of errors caused by repetitive mining over poor quality data (Cavoukian 1998). This increases the threat to the data subject and the costs associated with the identification and correction of the inaccuracies. Thus, when mining is repeatedly executed over personal data, frequent cleansing and updating efforts are appropriate. Solution: The adoption of data quality management strategies by the organisation, coupled with the expedient correction of any inaccuracies reported by individuals and intermittent data cleansing may go some way to resolving the dilemma. Other solutions are apparent (for example, data matching) but they may have unsatisfactory implications for privacy protection.

16. Data Mining Implications (Continued) Security issues Security is a big issue. Businesses store large quotas of information about their employees and customers including social security number, birthday, payroll and etc. However how properly this information is taken care of is still in question. There have been lot of cases where hackers accessed and stole big important data of customers from big corporation such as Ford Motor Credit Company, Sony… with so much personal and financial information available, identity theft becomes a big problem. Solution: The data security issue can be resolved by the introduction of application control mechanisms to MLS databases. One possibility is the employment of intelligent agent technology. A knowledge base of heuristic rules defining permitted and disallowed applications would need to be constructed. A multi-agent system could then be used to monitor and supervise applications in real time. As each user logs in, an agent would be assigned to regulate their queries and perhaps filter their results. The technical complexities of such a system would be a topic of further research.

17. CONCLUSION Data mining brings a lot of benefits to businesses, society, governments as well as the individual. However, privacy, security, and data accuracy are the big problems if they are not addressed and resolved properly.