Burton D. Morgan Entrepreneurial Competition

1. Burton D. Morgan Entrepreneurial Competition • Are you the entrepreneurial type? Do you want to start your own business and be your own boss? Do you have an idea for a new business? Then join us. We will help you: • formulate your ideas • create a business plan • get feedback and possibly funding from nationally known entrepreneurs and venture capitalists • get seed funding for your business in the form of prizes totaling at least $50,000 (possibly more) • get space in the Purdue Technology Incubator • The competition is open to all Purdue students. • Callouts on the 5th and 6th September, 7-9 pm in Krannert Auditorium. Register with paf@purdue.edu or call 4-7324 More information at www.mgmt.purdue.edu/entrep

2. CS 590M Fall 2001: Security Issues in Data Mining Lecture 6: Time Series, Regression, Data Mining Process

3. Regression • Problem: Prediction of Numerical Values • Similar to Classification, but continuous class • Strong Statistical base • Data mining community primarily concerned with scale

4. Regression: Problem Definition • Data: Sequence of vectors xi, yi, i=1,…,n • Goal: Find function f such that f(x)y for • Training data xi, yi • x, y where y is unknown • Note that f captures relationship between x and y, but doesn’t imply causality

5. Regression: Issues • Curse of dimensionality: As the number of attributes/values grows, • Space of possible functions f grows exponentially • Number of training examples needed to learn best f grows exponentially • Solution: Constrain space of possible functions

6. Regression: Approaches • Decision Trees • Regression Trees (e.g., CART) • Decision tree with automatic selection of number of choices at each node • Regression Splines (e.g., MARS) • Handles discontinuity at choice points • Artificial Neural Networks • Capable of computing arbitrarily complex functions

7. Time Series • Time/value data • Not sequential associations – value@time, not event@time • Generally viewed as a function with a value at any given time • Goals: • Learn function • Identify repeated patterns of value change

8. Time Series: Finding Patterns • Given a values over a time fragment, find time fragments with similar values given: • Shift of values • Scaling of values • Stretching of time • Find commonly occurring patterns of values (e.g., the time fragments that would give the most similar fragments under the above conditions)

9. Time Series: Approaches • Transformation • Use DFT to transform to frequency domain • Drop all but first few frequencies • Index in R* tree and search • Window-based • Sliding window across sequence • Index key features in special data structure • Count entries at each index point

10. Data Mining Process • Cross-Industry Standard Process for Data Mining (CRISP-DM) • European Community funded effort to develop framework for data mining tasks • Goals: • Encourage interoperable tools across entire data mining process • Take the mystery/high-priced expertise out of simple data mining tasks

11. CRISP-DM: Overview

12. CRISP-DM: Phases • Business Understanding • Understanding project objectives and requirements • Data mining problem definition • Data Understanding • Initial data collection and familiarization • Identify data quality issues • Initial, obvious results • Data Preparation • Record and attribute selection • Data cleansing • Modeling • Run the data mining tools • Evaluation • Determine if results meet business objectives • Identify business issues that should have been addressed earlier • Deployment • Put the resulting models into practice • Set up for repeated/continuous mining of the data