Data Mining By Jason Baltazar Phil Cademas Jillian

Data Mining By Jason Baltazar, Phil Cademas, Jillian Latham, Rachel Peeler & Kamila Singh

What is Data Mining? l Data Mining is data processing using sophisticated data search capabilities and statistical algorithms to discover patters and correlations in large preexisting databases. l 2 n Broad Categories: Supervised & Unsupervised

Unsupervised Data Mining l “Descriptive Modeling” l Uncover patterns and relationships among data l No predetermined parameters l Observations l Used after analysis to assist in making business decisions

Cluster Analysis l “Automated Data Mining” l Used to discover the segments or groups within a customer data set l Determine classes of similar customers that naturally fit together l Demographics l Segmented Markets n Marketing and Advertising

Supervised Data Mining l “Predictive Modeling” l Set goals and parameters prior to data mining l Concentration: only relevant patterns l Predict outcomes l Anomaly Detection, Classification & Prediction, Regression, Analysis

Anomaly Detection l Models built to specify “normal” ranges of results l Fraud Detection n Tax, insurance, credit card industries l Prevent Identity Theft l Detect breaches in computer security l Pay. Pal n 15% of all e-commerce in the U. S.

Classification & Prediction l Most common data analysis tool l “Who will buy what, and how much will they buy? ” l Credit analysis / Credit Scoring – Who are my “good credit risks? ” l Based on spending habits, income, and/or demographics l Can be used in customer segmentation, business modeling, credit analysis, etc.

Classification & Prediction l Human Resources l Turnover analysis, employee development, recruiting, training, and employee retention Determine the “value” of employees n Fill leadership/management positions from within the organization n Groom and promote based on a set of predetermined skills, attitudes, and competencies n

Regression Analysis l Statistics applies to data to make predictions n i. e. How product price and promotions affect sales l Marketing, pricing, product positioning, sales forecasting, advertising, human relations, customer service l Objectives: market response modeling and sales forecasting

Text Mining l Text Mining is the process of automatically processing text and extracting information from it l Presidential election

Text Mining Applications l Security Applications l Biomedical l Online Applications Media Applications l Academic Applications

Data Mining Advantages l Helps to reduce costs l Provides improved and more detail oriented service l Increases market effectiveness l Beneficial to all industries

Data Mining Disadvantages l Privacy Issues n l Security Issues n l Access to personal information Insufficient security systems Misuse of information & inaccurate information

Insurance & Healthcare l Target l Helps marketing to develop different plans and policies

Mobile Communication l Helps develop a variety of different cell phone plans l Target marketing

Data Mining Privacy l Who has access to consumer personal information n CVS Pharmacy & Marketing Companies

Data Mining Ethics: Consumers l How far is too far? n Trustworthy? l Data is being collected & used l Opt out boxes n What are some solutions that give consumers control? Access to databases that have their information l The right to change what information is available l

Data Mining Ethics: Businesses l Help enhance overall customer satisfaction n Profit enhancer? n Violation of privacy l Sometimes companies n They partnered with marketing also have access to private information

Conclusion ANY QUESTIONS?