%0 Journal Article %1 Wakode_2015 %A Wakode, Avisha %A Chaudhari, Mrs. D. A. %D 2015 %I Auricle Technologies, Pvt., Ltd. %J International Journal on Recent and Innovation Trends in Computing and Communication %K Aggregation CASE Dataset PIVOT SPJ %N 4 %P 1998--2002 %R 10.17762/ijritcc2321-8169.150451 %T Improved Data Mining Analysis by Dataset creation using Horizontal Aggregation and B Tree %U http://dx.doi.org/10.17762/ijritcc2321-8169.150451 %V 3 %X Data Mining is one of the emerging field in Research and information retrieval. Data mining tools requires data in the form of data set. Data set preparation is one of the important task in data mining. Data set is collection of data which is stored in relational database where database schema are highly normal- ized. To analyze data efficiency, data mining systems are widely using datasets with columns in horizontal tabular layout. The two main components of sql code is join and aggregation Vertical aggregations have limitations to build data sets because they return one column for aggregated group using group functions. Preparing a data set for data mining analysis is generally the most tedious and time consuming task in a data mining project, which requires many complex SQL queries, joining tables and columns, and aggregating columns. A powerful methods to generate SQL code to return aggregated columns in a horizontal or cross tabular form, returning a set of numbers instead of one number per row is introduced. This new class of methods is called horizontal aggregations. Horizontal aggregations are evaluted using three functions : CASE, SPJ and PIVOT method.Data mining also deals with searching of information. This paper focuses on creation of B+ tree to reduce the time of information search so that efficiency of the system increases.

@article{Wakode_2015, abstract = {Data Mining is one of the emerging field in Research and information retrieval. Data mining tools requires data in the form of data set. Data set preparation is one of the important task in data mining. Data set is collection of data which is stored in relational database where database schema are highly normal- ized. To analyze data efficiency, data mining systems are widely using datasets with columns in horizontal tabular layout. The two main components of sql code is join and aggregation Vertical aggregations have limitations to build data sets because they return one column for aggregated group using group functions. Preparing a data set for data mining analysis is generally the most tedious and time consuming task in a data mining project, which requires many complex SQL queries, joining tables and columns, and aggregating columns. A powerful methods to generate SQL code to return aggregated columns in a horizontal or cross tabular form, returning a set of numbers instead of one number per row is introduced. This new class of methods is called horizontal aggregations. Horizontal aggregations are evaluted using three functions : CASE, SPJ and PIVOT method.Data mining also deals with searching of information. This paper focuses on creation of B+ tree to reduce the time of information search so that efficiency of the system increases.}, added-at = {2015-08-21T07:46:26.000+0200}, author = {Wakode, Avisha and Chaudhari, Mrs. D. A.}, biburl = {https://www.bibsonomy.org/bibtex/2d0bce5b36f387f73a92be9f2d0a04d25/ijritcc}, doi = {10.17762/ijritcc2321-8169.150451}, interhash = {21dbeb6dbd72c3ae647479a436c67749}, intrahash = {d0bce5b36f387f73a92be9f2d0a04d25}, journal = {International Journal on Recent and Innovation Trends in Computing and Communication}, keywords = {Aggregation CASE Dataset PIVOT SPJ}, month = {april}, number = 4, pages = {1998--2002}, publisher = {Auricle Technologies, Pvt., Ltd.}, timestamp = {2015-08-21T07:46:26.000+0200}, title = {Improved Data Mining Analysis by Dataset creation using Horizontal Aggregation and B Tree}, url = {http://dx.doi.org/10.17762/ijritcc2321-8169.150451}, volume = 3, year = 2015 }