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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

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Burton D. Morgan Entrepreneurial Competition

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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

  • CS 590M Fall 2001: Security Issues in Data Mining

    Lecture 6: Time Series, Regression, Data Mining Process


    • 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

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