2.  Data mining is sorting through data to identify patterns and establish relationships  Sifting through very large amounts of data for useful information  Business Sections Using Data Mining : Data MiningUsed in FinancialUse in various task by banks, investment funds, insurance companies RetailFraud detection, market basket analysis, sales forecasting HealthcarePharmacy, healthcare insurance

4. Case Study Of Data Mining Process :  Currently, the Korean market might be represented from 2mainstreams : 1.The supply exceeds the demand 2.The competition around the various kinds of the business is getting acute  They are caused from a variety of the customer needs and increasing of ones  According to them, the various media such as the internet marketing, cell phone marketing, and call center are coming to us. Due to a variety of the marketing media, enterprises have more opportunities to touch with customers  In addition, the necessities to take advantage of these opportunities there are various contact points with customers come to be important

5. Issue : With the data warehouse establishment and requirements of using the data warehouse in order to support marketing, database marketing(DBM), data mining(DM), and customer relationship management(CRM) Purpose Data Mining : Technique to enable to discover knowledge from a deluge of data, is used in an executed project in order to support decision making of an enterprise. This scoring model is for selecting a target group who is likely to purchase a specific product In this project, we applied CRISP -DM methodology and data mining goal is “cross product sales to existing customers and give useful insights into the relationships”. Link : http://isi.cbs.nl/iamamember/CD2/pdf/269.PDFhttp://isi.cbs.nl/iamamember/CD2/pdf/269.PDF

8.  Is the process of searching, arrange and deriving high-quality useful material from text sources  It needs setting up patterns in text files, deriving rule patterns, applying them to the text, and take the output as meaningful information  As most information (over 80%) is stored as text  text mining is a very useful technique to find customer information from the unstructured text  Text mining can be challenging because natural language text is often inconsistent. It contains unclear caused by slang and syntax

9.  Typical Applications for Text Mining : i) Analyzing open-ended survey responses ii) Automatic processing of messages, emails, etc iii) Automatic processing of messages, emails, etc iv) Investigating competitors by crawling their web sites

10. Step 1 : Word information passed through text preprocessor

11. Step 2 : Language translated into Numbers using Equity Decision System’s text mining techniques to create text relationship models

12. Step 3 : Mathematical and statistical techniques are applied to derive clusters

13.  TYPES : 1. Web usage mining 2. Web content mining 3. Web structure mining

14. 1. Web usage mining ▪ Process of extracting useful information from server logs i.e users history. ▪ The process of finding out what users are looking for on the Internet. Some users might be looking at only textual data, whereas some others might be interested in multimedia data. 2. Web content mining ▪ The process to discover useful information from text, image, audio or video data in the web. ▪ Sometimes is called web text mining, because the text content is the most widely researched area.

15. 3. Web structure mining ▪ Process of using graph theory to analyze the node and connection structure of a web site. ▪ Two kinds: 1.Extracting patterns from hyperlinks in the web: a hyperlink is a structural component that connects the web page to a different location. 2.Mining the document structure: analysis of the tree-like structure of page structures to describe HTML or XML tag usage.

16. Pros  Enabled ecommerce to do personalized marketing, which eventually results in higher trade volumes.  The government agencies are using this technology to classify threats and fight against terrorism.  The predicting capability of the mining application can benefits the society by identifying criminal activities.

17.  The companies can establish better customer relationship by giving them exactly what they need. 1. Companies can understand the needs of the customer better and they can react to customer needs faster. 2. The companies can find, attract and retain customers; they can save on production costs by utilizing the acquired insight of customer requirements. 3. They can increase profitability by target pricing based on the profiles created. 4. They can even find the customer who might default to a competitor the company will try to retain the customer by providing promotional offers to the specific customer, thus reducing the risk of losing a customer or customers.

18. Cons  The invasion of privacy.  Privacy is considered lost when information concerning an individual is obtained, used, or disseminated, especially if this occurs without their knowledge  The obtained data will be analyzed, and clustered to form profiles.  The data will be made anonymous before clustering so that there are no personal profiles.  Thus these applications de-individualize the users by judging them by their mouse clicks.

19.  De-individualization  a tendency of judging and treating people on the basis of group characteristics instead of on their own individual characteristics and merits.  kecenderungan menilai dan melayan orang berdasarkan ciri-ciri kumpulan bukan pada ciri-ciri individu mereka sendiri dan manfaat.

20. Business Intelligence search engine + BI search

21.  Advantages  Ease of use – enabling end users to use computers the way they do in their personal lives, whether by searching for vacation getaways, conducting price comparisons or simply shopping online  Access to information – giving end users the ability to find BI related content without requiring the knowledge of where data resides, the names of reports, or what information is currently available  BI for the masses – allowing all decision makers in the organization information access

22.  Advantages  Broader use of business intelligence – providing diversity of use to give end users options of how they access BI  Integration with organization wide processes – expanding search within BI or adding search to access business intelligence documents. Embedding search technology within organizations has become commonplace within content or document management systems, library systems, etc.

23.  BI search enables productivity and efficiency gains, by helping end users find reports and information faster.  Some companies seem to agree, with 62% of TDWI's survey respondents rating the perceived business value of BI search as high or very high.  BI search enables a wide variety of self-service capabilities that offload work from IT to the report-consumers

24. USING BI SEARCH TO CREATE ORGANIZATION WIDE ACCESSIBILITY TO BI Wiith the combination of search and integrated portals, and by using social media and Web 2.0 concepts as a guide, BI is slowly moving towards actual organization-wide implementations. Included in this change are embedded BI (BI within business processes – where in some cases end users don’t even know they are using BI) and “pervasive BI” or BI for the masses (BI being deployed to and used by all decision makers across the organization). What this means for organizations is that long-term success and organization- wide deployments of business intelligence solutions may depend upon developing and deploying BI in a way that matches personal computer use.

25.  http://www.dashboardinsight.com/articles/ne w-concepts-in-business-intelligence/using-bi- search-to-create-organization-wide- accessibility-to-bi.aspx