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by: Floy Kub

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8

# Statistical Models Theory and Application STAT 215B

Marketplace > University of California - Berkeley > Statistics > STAT 215B > Statistical Models Theory and Application
Floy Kub

GPA 3.64

Staff

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COURSE
PROF.
Staff
TYPE
Class Notes
PAGES
8
WORDS
KARMA
25 ?

## Popular in Statistics

This 8 page Class Notes was uploaded by Floy Kub on Thursday October 22, 2015. The Class Notes belongs to STAT 215B at University of California - Berkeley taught by Staff in Fall. Since its upload, it has received 63 views. For similar materials see /class/226728/stat-215b-university-of-california-berkeley in Statistics at University of California - Berkeley.

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Date Created: 10/22/15
Statistics 215b 112003 DR Brillinger Data mining A field in search of a definition a vague concept D Hand H Mannila and P Smyth 2001 Principles of Data Mining MIT Press Cambridge Some definitionsdescriptions Data mining is the analysis of often large observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner Hand et al N data mining refers to extracting or mining39 knowledge from large amounts of data N data mining should have been more appropriately named knowledge mining from data An analytic process designed to explore data usually large amounts of data typically business or market related in search of consistent andor systematic relationships between variables and then to validate the findings by applying the detected patterns to new subsets of data Data mining is the application of a specific algorithm usually within machine learning for extracting patterns from data A simple approach to the theory of data mining is to declare that data mining is statistics perhaps on larger data sets than previously m Data mining is the nontrivial process of identifying valid novel potentially useful and ultimately understandable patterns in data Data mining is the process of extracting previously unknown comprehensible and actionable information from large databases and using it to make crucial business decisions Data mining is a set of methods used in the knowledge discovery process to distinguish previously unknown relationships and patterns within data Data mining is the process of discovering advantageous patterns in data Data mining is a decision support process where we look in large data bases for unknown and unsuspected patterns of information Data mining is the process of seeking interesting or valuable information within large databases Data mining is the exploration and automatic analysis of large data sets by automatic or semiautomatic means with the purpose of discovering meaningful patterns Data mining the science of extracting useful information from large data sets Data mining is a knowledge discovery process of extracting previously unknown actionable information from very large databases Data mining is finding interesting structure patterns statistical models relationships in databases Data mining is a process that uses a variety of tools to discover patterns and relationships in data that may be used to make valid predictions Data mining The process of efficient discovery of nonobvious valuable patterns from a large collection of data Data mining is exploratory data analysis with little or no human interaction using computationally feasible techniques ie the attempt to find interesting structure unknown a priori Data mining is the art and science of teasing meaningful information and patterns out of large quantities of data Data mining is in fact an umbrella term for a variety of analytic techniques Data mining is about digging into data to find subtle patterns and informative relationships amongst the data resources piling up in today s businesses Data mining the extraction of hidden predictive information from large databasesm The knowledge discovery and data mining KDD field draws on the findings from statistics databases and artificial intelligence to construct tools that let users gain insight from massive data sets Data mining Objective explore the data for interesting patterns Approach size is overcome to search for information Criteria data become findings by obtaining questions Focus association is sought through coincidence Stats sampling and design aid start Sequential analysis aids end Process reduce the data with maximum gain Behavior break old rules powered by technology Attitude seek local effects proactively amp persistentlyquot Arnold Goodman Data mining refers to the exaggerated claims of significance and or forecasting precision generated by the selective reporting of results obtained when the structure of the model is determined experimentally by repeated applications of such procedures as regression analysis to the same body of data m synonymous with data scrubbing data fishing Darwinian econometrics survival of the fittest m the term data mining is sometimes used perjoratively to describe such work particularly when an analyst has searched over a large model space without adjusting for such a search or testing the resulting model on new data Data mining fishing grubbing number crunching These are value laden terms we use to disparage each other s empirical work with the linear regression model A less provocative description would be quotspecification searchquot and a catch all definition is the data dependent process of selecting a statistical model mining suggests that the activity may in fact be productive Tukey N data analysis which I take to include among other things procedures for analyzing data techniques for interpreting the results of such procedures ways of planning the gathering of data to make its analysis easier more precise or more accurate and all the machinery and results of mathematical statistics which apply to analyzing data

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