Data mining.pptx

BUSINESS INTELLIGENCE DATA MINING 1 What is Data Mining  A process that uses statistical, mathematical, artificial intelligence and machine learning techniques (sophisticated, advanced data manipulation technology) to extract and identify useful information and subsequent knowledge from large...

BUSINESS INTELLIGENCE DATA MINING 1 What is Data Mining  A process that uses statistical, mathematical, artificial intelligence and machine learning techniques (sophisticated, advanced data manipulation technology) to extract and identify useful information and subsequent knowledge from large database. Uses sophisticated data manipulation technology Data Mining Deals with large databases Identifies useful information 2 Data Mining Concepts and Applications  Where is Data Mining in Business Intelligence? 3 Why do we need Data Mining?  Users today want to perform statistical and mathematical analysis such as hypothesis testing, prediction and customer scoring models  A major step in managerial decision making is forecasting or estimating the results of different alternative courses of actions  Such investigation cannot be done with basic OLAP and will require special tools – advanced business analytics – data mining 4 Why do we need Data Mining? OLAP Data Mining Which branch in the northern region has obtained the poorest customer feedback during the New Year seasons in the last three years. Which electrical product will be the most suitable to be bundled together with the sale of the newly introduced washing machine? 5 Major Characteristics of Data Mining  Data are often buried deep within very large databases, which sometimes contain data from several years.  Sophisticated tools are used to clean and synchronize data in order to get the best result.  Miners may find an unexpected result during data mining activities and this will require creative thinking on the users’ decision making 6 DATA MINING METHODS Prediction Methods: using some variables to predict unknown or future values of other variables.  Descriptive Methods: finding human- interpretable patterns describing the data 7 Data Mining Tasks\Algorithms(fall Into Four Broad Categories): Classification  Clustering Association Rule Discovery  Sequential Pattern Discovery 8 Data Mining Tasks\Algorithms 1. Classification – – – – Also known as supervised induction, most common of all data mining activities. Medical Insurance company: E.g. Clients with a history of diabetes Used to analyze the historical data (from stored in the database and to maternal/paternal automatically generate a model that side) are likely to also can predict future behavior. have diabetes in a Identify patterns of data to belong to later stage of his/her life. a certain category Decision: A special premium coverage Application example : target can be designed for marketing (likely customer or no 9 the potential health hope, based on the previous condition customers’ behavior) Mining 2. Data Clustering algorithms(Fall into four broad categories): – Partitioning a database into segments in which the members of a segment share similar qualities – Unlike classification, the cluster is unknown when the algorithm starts. – Clustering technique includes optimization, the goal is to create groups so that members within each group have maximum similarity and the members across groups have minimum similarity – Before the results of clustering techniques are used, it might be necessary for an expert to interpret, modify the information – Comb the whole data to identify sharing of similar qualities/ characteristics and create group based on that: E.g. Payment by credit card is more popular in the urban area compared to the rural area. Decision: Demographically, the social class determines the method of payment. This can be interpreted 10 into business decisions /strategy. Classifying vs. Clustering What is the major difference between cluster analysis and classification?  Classification is sorting cases into groups so that members of the same group are strongly associated in some meaningful way.  Cluster analysis identifying the common characteristics shared by members of groups in transactions, and interpret that into a case. 11 Data Mining algorithms(Fall into four broad categories): 3. Association – Establishes relationship about items that occur together in a given record – Determining associations among items that sell together – Often called market basket analysis as the primary applications is the analysis of sales transactions – Application example : Market basket analysis Placing batteries in the toys If a customer buys bread, they are also likely to buy milk 12 Data Mining algorithms(Fall into four broad categories): 4. Sequence discovery – The identification of association over time – Some sequence discovery techniques keep track of elapsed time between associated events and the frequency of occurrences – Application example : Market basket analysis over time, customer life cycle analysis Unemployed consumer who purchased pre paid telco service are most likely to convert to postpaid upon being employed Purchase of machinery will later be followed by the purchase of maintenance service 13 14 Types of data mining (Two types) 1) Hypothesis-driven data mining Begins with a proposition by the user, who then seeks to validate the truthfulness of the proposition e.g. Start with a statement - The cause of fire during road accident is due to the modification of vehicle by an unauthorized parties, then use data mining to prove the statement 2) Discovery-driven data mining Finds patterns, associations, and relationships among the data in order to uncover facts that were previously unknown or not even contemplated by an organization15 Use in Business Business Use Banking Forecasting levels of bad loans, fraud in credit card usage,  Where data mining is beneficial (the intent most of these credit card spending pattern, new in loans examples is to identify a business opportunity and create a Retailing and Predicting sales, determining sustainable competitive advantage). Fillcorrect in the inventory blanks. levels and sales distribution schedules Manufacturing and production Predicting when to expect machinery failures Marketing Predicting which customers will respond to Internet banners or buy a particular products 16 Use in Business Business Use Government Forecasting threats to national security, predicting  Where data mining is beneficial (the intent in most of these and defense resources consumptions examples is to identify a business opportunity and create a sustainable competitive advantage) of patients with critical illnesses. Health Correlating demographics Doctors will be more prepared Airlines Capturing popular and unpopular routes at given times Broadcasting Predicting what programm are best shown at prime time, and which is the best time to slot in advertisement. 17 Understanding Customer Behavior For most retail environments, three sources of customers data are most critical to data mining efforts aimed at better understanding of behavior: – Demographic data – salary, population – Transaction data – purchase type, online, cash, credit – Online interaction data - favorite sections in website (clickstream analytics can be used to identify who did/did not buy product, why and when) 18 Data Mining in Retail  Data mining in retail usually is looking at three different aspects: 1. Web analytics – Gather web statistics that track customer’s online behavior ; hit, pages, sales, volume, and so on. This helps in adjusting a web site to meet customer needs. 2. Customer analytics – transaction data from offline purchases, sales and orders made, call for support, and demographic data. This is critical in CRM and revenue management because a better understanding allows an organization to cluster customers into groupings. 3. Optimization – Patterns can be detected and used to optimize transaction and customer interaction. For example in recommending relevant styles and complementary19 purchases/products to suit customer behavior Text Mining  Application of data mining to nonstructured or less structured text files.  It generates meaningful numerical indices from the unstructured text and then processes these indices using various data mining algorithms Data Mining Text Mining Takes advantage of the infrastructure of stored data to extract additional useful information. E.g. Applying data mining to customer database, we may discover that everyone who buys product A will also buy products B and C six months later Operates with text documents - less structured information.. E.g. Visualizing relationships between documents such as policies, memos, emails, minutes of meeting etc. Organizations recognized this as one of the major sources for competitive advantage. 20 Text Mining Example • Airline industry uses text mining software to focus on key problem areas through pattern identification by accessing incident reports to increase the quality of service. – The most frequently occurring terms are identified through incident reports documented . – Cluster/group the terms e.g. the term spillage and associate with other key terms such as coffee, tea, soup, drink 21 – Can identify incidents that might lead to trouble and help management stop the issue Text Mining Example  A private tertiary institution uses text mining to establish knowledge on programs offered by the competitors by accessing the advertisement materials produced by the competitors  The most frequently occurring terms are identified through the advertisements  Cluster/group the terms e.g. the term degree and associate with other key terms such as 2+1, 3+0, accommodation, fees  Can identify new programs or types of facilities offered by the competitors 22 Text Mining How to mine text 1. Eliminate commonly used words (e.g. the, and, other). These are known as stop-words. 2. Replace words with their stems or roots (e.g. eliminate plurals and various conjugations). The terms phoned, phoning, and phones would be mapped to phone. 3. Consider synonyms and phrases. Synonyms need to be combined, e.g students and pupil need to be grouped together. 23 Text Mining How to mine text 4. Calculate the weights of the remaining terms, looking at the frequency with which the words appear 2 common measures are used for this, 1) Term frequency factor (the actual number of times the word appears in a document) and 2) Inverse document frequency (the number of times the word appears in all document in a set) – If tf factor is large, weight increase, If idf factor is large, weight decrease – Reason: idf indicates that the terms would be a common words to the industry. 24 Web Mining The discovery through the analysis of interesting and useful information from the web, about the web and usually using a web based tool. 25 Types of Web Mining 1. Web content mining - extraction of useful information from Webpages. May be used to enhance search results produced by search engines 2. Web structure mining – generating information from the links included in WebPages. Can be used to structure the display of the page. Can also identify the members of specific communities and their roles 3. Web usage mining – generated through web page visits, transactions and web server logs – useful for CRM, understanding user behavior (web analytics) 26