Corpus ID: 19113952

Data Mining in Excel: Lecture Notes and Cases

  title={Data Mining in Excel: Lecture Notes and Cases},
  author={Galit Shmueli},

Topics from this paper

Data Mining, Machine Learning and Big Data Analytics
This paper analyses deep learning and traditional data mining and machine learning methods; compares the advantages and disadvantage of the traditional methods; introduces enterprise needs, systemsExpand
Heterogeneous Data and Big Data Analytics
The benefits of the confluences among Big Data analytics, deep learning, high performance computing (HPC), and heterogeneous computing are presented and challenges of dealing with heterogeneous data and Big data analytics are discussed. Expand
Financial Distress Prediction for Small and Medium Enterprises Using Machine Learning Techniques
Financial distress prediction is a key challenge every financing provider faces when determining borrower creditworthiness. Inherent opaqueness of Small and Medium Enterprise business complicatesExpand
Data analytics for network intrusion detection
It is shown that HMM-based analytics can achieve the best accuracy in the spam-email classification although only a few features are used in the HMM while all features areused in the Naïve Bayesian classification and deep learning. Expand
Ein Vergleich von Datenanalysemethoden für eine Affective Engineering Methode
Different quality indicators unveil characteristics which the designer may use to mine their potential for future AE analyses, and anal-yses of nonlinear regression, artificial neural networks, fuzzy logic systems and hybrids are examined. Expand
Temperature is one of the basic components of the weather. In this paper, mean temperature have been forecasted using Artificial Neural Network (ANN). The design of the ANN based on four weatherExpand
Classification of road users detected and tracked with LiDAR at intersections
Lizarazo Jiménez, Cristhian Guillermo. M.S.C.E., Purdue University, December 2016. Classification of road users detected and tracked with LiDAR at intersections. Professor: Andrew Tarko. DataExpand
Data mining classification techniques: an application to tobacco consumption in teenagers
This study is aimed at analysing the predictive power of different psychosocial and personality variables on the consumption or non- consumption of nicotine in a teenage population using differentExpand
Development of automated dynamic bidding agents for final price prediction in online auctions
........................................................................................................................... 1 Chapter 1Expand
Microfinance and Women Household and Micro Business Performance: From the Perspective of Malaysia
The role of microfinance on poverty reduction and socioeconomic development has become a buzzword in the economic credit market. Yet, the impact of microfinance is still questioned because theExpand


Mastering Data Mining
  • E. Ziegel
  • Computer Science, Mathematics
  • Technometrics
  • 2001
This book deals in passing with the relationship of both statistics and statisticians to the datamining process and certainly promotes a role for the statistically knowledgeable participant in the data mining effort. Expand
Regression Analysis by Example
Simple Linear Regression Multiple Linear Regression Regression Diagnostics: Detection of Model Violations Qualitative Variables as Predictors Transformation of Variables Weighted Least Squares TheExpand
Data Mining - Concepts and Techniques
  • P. Perner
  • Computer Science
  • Künstliche Intell.
  • 2002
Data Mining Explained
  • 2001
Mining associations between sets of items in massive databases
  • Proceedings of the 1993 ACM-SIGMOD International Coference on Management of Data (pp. 207-216), New York: ACM Press
  • 1993
On the Analysis of Qualitative Data in Marketing Research
Logit and log-linear models are new techniques for analyzing categorical data. Each of these models is described and applied to a problem involving consumer adoption of a new telecommunicationsExpand
Estimation of Time-Response Curves and Their Confidence Bands
The study of a biological process over a period of time is often of interest in physiological and medical investigations. Important examples of this are afforded by curves describing secretion andExpand
Principal Curves
Principal curves are smooth one-dimensional curves that pass through the middle of a p-dimensional data set, providing a nonlinear summary of the data. They are nonparametric, and their shape isExpand
Principles of Data Mining
This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions. Expand
How Much Information
  • Retrieved from on Nov
  • 2003