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 4-7324More information at www.mgmt.purdue.edu/entrep

CS 590M Fall 2001: Security Issues in Data Mining

Lecture 6: Time Series, Regression, Data Mining Process

Regression

• Problem: Prediction of Numerical Values– Similar to Classification, but continuous

class

• Strong Statistical base

• Data mining community primarily concerned with scale

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

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

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

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

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)

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

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

CRISP-DM: Overview

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