MATLAB BASICS
1. Matlab Desktop and Help
2. Matlab Variables
Matlab stores the variables in a part of memory called .
By default a variable is of . type
What is the meaning of ; (semicolon) at the end of a Matlab instruction?
Calculate 21000 ; then calc
DataMining:Data
Lecture Notes for Chapter 2
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
WhatisData?
q
Collection of data objects and
their attributes
q
An attribute is a property or
ch
DataMining:ExploringData
Lecture Notes for Chapter 3
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
8/05/2005
1
Whatisdataexploration?
A preliminary exploration of the data to
better understand its c
DataMining
Classification:BasicConcepts,Decision
Trees,andModelEvaluation
Lecture Notes for Chapter 4
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
Classification:Definition
q
Given a co
DataMining
Classification:AlternativeTechniques
Lecture Notes for Chapter 5
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
RuleBasedClassifier
q Classify
records by using a collection of
DataMining
AssociationAnalysis:BasicConcepts
andAlgorithms
Lecture Notes for Chapter 6
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
AssociationRuleMining
q
Given a set of transactions,
Data Mining Association Rules: Advanced Concepts and Algorithms Lecture Notes for Chapter 7 Introduction to Data Mining
by Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
Continuous and Categorical Attributes
How to app
DataMining
ClusterAnalysis:BasicConcepts
andAlgorithms
Lecture Notes for Chapter 8
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
WhatisClusterAnalysis?
q
Finding groups of objects such t
import sys
import random
import pickle
from mrjob.job import MRJob
import os
class MRMatrixMult2Pass(MRJob):
# def configure_options(self):
#
super(MRMatrixMult2Pass, self).configure_options()
#
self.add_passthrough_option('-matrix1', help="Filename of ma
CS 4432
Database Systems II
Logistics
CS4432
Notes 1
1
Course Web Site
WEB SITE:
http:/www.cs.wpi.edu/~cs4432/c12
MyWPI SITE:
http:/my.wpi.edu
Staff
INSTRUCTOR: Professor Elke Rundensteiner
Office Hours:
Mondays, 12pm 1pm ,
and Thursdays 4:00pm 5:0
DataMining:Introduction
Lecture Notes for Chapter 1
Introduction to Data Mining
by
Tan, Steinbach, Kumar
Tan,Steinbach, Kumar
Introduction to Data Mining
4/18/2004
1
WhyMineData?CommercialViewpoint
q
Lots of data is being collected
and warehoused
Web da
Naive Bayesian
Page 1 of 3
Map > Data Mining > Predicting the Future > Modeling > Classification > Naive Bayesian
Naive Bayesian
The Naive Bayesian classifier is based on Bayes theorem with independence assumptions between predictors. A Naive
Bayesian mod
Decision Tree
Page 1 of 4
Map > Data Mining > Predicting the Future > Modeling > Classification > Decision Tree
Decision Tree - Classification
Decision tree builds classification or regression models in the form of a tree structure. It breaks down a datas
CS4445 Data Mining and Knowledge Discovery in
Databases. A-2004
SOLUTIONS Exam 1 by Prof. Carolina Ruiz - September
17, 2004
Prof. Carolina Ruiz
Department of Computer Science
Worcester Polytechnic Institute
Problem I. Decision Trees (30 points)
Consider
CS4445 Data Mining and Knowledge Discovery in
Databases. A-2004
Solutions Exam 2 - October 14, 2004
By Prof. Carolina Ruiz
Department of Computer Science
Worcester Polytechnic Institute
Instructions
Show your work
Justify your answers
Use the space pro
CS4445 Data Mining and Knowledge Discovery in Databases. B Term 2006
Solutions Exam 2 - December 12, 2006
By Prof. Carolina Ruiz
Department of Computer Science
Worcester Polytechnic Institute
Problem I. Numeric Predictions (45 points)
Consider the followi
CS4445 Data Mining and Knowledge Discovery in Databases. B Term 2006
Solutions Exam 2 - December 12, 2006
By Prof. Carolina Ruiz
Department of Computer Science
Worcester Polytechnic Institute
Problem I. Numeric Predictions (45 points)
Consider the followi
CS4445 Data Mining and Knowledge Discovery in Databases. A Term 2008 Exam 1 September 26, 2008 SOLUTIONS
Prof. Carolina Ruiz Department of Computer Science Worcester Polytechnic Institute NAME: Prof. Ruiz Problem I: Problem II: Problem III: Problem IV:
CS 4445 Data Mining and Knowledge Discovery in Databases
Exam Topics and Sample Questions - B Term 2010
PROF. CAROLINA RUIZ
Topics covered by Exam 1 and sample exam questions:
1. Introduction: CHAPTER 1 + Class Handout + Textbook Slides
o Define Data Mini
CS4445 Data Mining and Knowledge Discovery in Databases. B Term 2010
Exam 1 - November 19, 2010
Prof. Carolina Ruiz
Department of Computer Science
Worcester Polytechnic Institute
NAME: Carolina Ruiz and Yutao Wang
Instructions:
Show your work and justify
Homework 1: Solutions
CS4445/B12
Provided by: Kenneth J.
Entropy of the original set
genre
comedy
comedy
comedy
action
action
comedy
comedy
drama
drama
drama
drama
action
action
action
critics-reviews
thumbs-up
thumbs-up
neutral
thumbs-down
neutral
thumbs
Homework 3: Solutions
CS4445/B12
Provided by: Kenneth J.
Topology of the Network
This is the nave topology, we can easily
construct the graph for this dataset.
likes
genre
criticsreview
s
rating
IMAX
Then we need to calculate the conditional probability
t
CS4432: Database Systems II
Data Storage (2)
(Sections 13.1 13.3)
Elke A. Rundensteiner
Data Storage: Overview
How does a DBMS store and
manage large amounts of data?
(today)
What representations and data
structures best support efficient
manipulations