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Data Sets for Data Mining Analysis ABSTRACT Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables, and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g., point-dimension, observationvariable, instance-feature), which is the standard layout required by most data mining algorithms.
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WAY TO YOUR GOAL
DATAMINING PROJECT
FINAL YEAR PROJECTS
IEEE PROJECTS 2013-2014
CONTACT: 9940098300, 9500075001
E-Mail:[email protected]
Horizontal Aggregations in SQL to Prepare
ZEBROS PROJECTS
Office Address: No 4 / Flat No 3D, Sai Kiran Apts, First Main Road, Kasturba Nagar, Chennai-20 web: www.zebros.in e mail : [email protected] mob: 99400 98300
Data Sets for Data Mining Analysis
ABSTRACT
Preparing a data set for analysis is generally the most time consuming task in a
data mining project, requiring many complex SQL queries, joining tables, and
aggregating columns. Existing SQL aggregations have limitations to prepare data sets
because they return one column per aggregated group. In general, a significant manual
effort is required to build data sets, where a horizontal layout is required. We propose
simple, yet powerful, methods to generate SQL code to return aggregated columns in a
horizontal tabular layout, returning a set of numbers instead of one number per row.
This new class of functions is called horizontal aggregations. Horizontal aggregations
build data sets with a horizontal denormalized layout (e.g., point-dimension,
observationvariable, instance-feature), which is the standard layout required by most
data mining algorithms. We propose three fundamental methods to evaluate horizontal
aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on
standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator,
which is offered by some DBMSs. Experiments with large tables compare the proposed
query evaluation methods. Our CASE method has similar speed to the PIVOT operator
and it is much faster than the SPJ method. In general, the CASE and PIVOT methods
exhibit linear scalability, whereas the SPJ method does not.
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The Institute of Electrical and Electronics Engineers or IEEE (read eye-triple-e) is Incorporated in the State of New York, United States. It was formed in 1963 by the merger of the Institute of Radio Engineers (IRE, founded 1912) and the American Institute of Electrical Engineers (AIEE, founded 1884). A membership organization comprised of engineers, scientists and students that sets standards for computers and communications. It is a nonprofit organization with more than 365,000 members in around 150 countries.
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0th Review 1st Review
Abstract Existing System Disadvantages Proposed System Advantages Objective System Requirements System Architecture
Literature Survey Module List Module Description Data Flow Diagram Level DFD Module Wise DFD Problem Definition Review Document Explanation
2nd Review 3rd Review
Use case Diagram Class Diagram Collaboration Diagram Sequence Diagram Activity Diagram Testing & test cases Partial Code Screenshot for First two
module Review Document Explanation
Conclusion References Future Enhancement 65% code (Executable Format) Required Software Review Document Explanation
Final Review
Complete Code with Enhancement
Final Document (University Standard Format)
Complete Explanation for Project Concept & Code