| 000 -LEADER |
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| fixed length control field |
05764nam a2200313Ia 4500 |
| 001 - CONTROL NUMBER |
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| control field |
76987 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
ft6094 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251105180021.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
190313n 000 0 eng d |
| 040 ## - CATALOGING SOURCE |
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| Description conventions |
rda |
| 041 ## - LANGUAGE CODE |
|---|
| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76 L38 2015 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
. |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Regine P. Laurente and Errol John P. Yatar. |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Further enhancement of Apriori algorithm applied to market basket analysis using E-mart mobile application |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2015 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Other physical details |
Undergraduate Thesis: (BSCS major in Computer Science) -Pamantasan ng Lungsod ng Maynila, 2015. |
| 336 ## - CONTENT TYPE |
|---|
| Content type code |
. |
| Content type term |
text |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Materials specified |
0 |
| Media type code |
. |
| Media type term |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Materials specified |
0 |
| Carrier type code |
. |
| Carrier type term |
volume |
| Source |
rdacarrier |
| 385 ## - AUDIENCE CHARACTERISTICS |
|---|
| Audience term |
2 |
| 505 ## - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
ABSTRACT: Data mining is the process of automatically discovering useful information in large data repositories. Data mining techniques are deployed to scour large database in order to find novel and useful patterns that might otherwise remain unknown. They also provide capabilities to predict the outcome of a future observation. Data mining is a method of extracting what is useable within a database and separating it out from what is unusable . Such methods are necessary because, as human being, we lack the capacity to sort and organize such large volumes of data. In data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. Association rules are the main technique to determine the frequent item set in data mining. It is sometimes referred to as “Market Basket Analysis”. Market Basket Analysis or Association Analysis is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of items. It is used to analyze the customer purchasing behavior and helps in increasing the sales and maintain inventory by focusing on the point of sale transaction data. It targets customer baskets in order to monitor buying patterns and improve customer satisfaction. By analyzing, recurring patterns in order to offer related goods together can be found and therefore the sales can be increased. Sales on different levels of goods classifications and on different customer segments can be tracked easily. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work as a broad area for the researchers to develop a better data mining algorithm. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
5 |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
ABSTRACT: Data mining is the process of automatically discovering useful information in large data repositories. Data mining techniques are deployed to scour large database in order to find novel and useful patterns that might otherwise remain unknown. They also provide capabilities to predict the outcome of a future observation. Data mining is a method of extracting what is useable within a database and separating it out from what is unusable . Such methods are necessary because, as human being, we lack the capacity to sort and organize such large volumes of data. In data mining, Apriori is a classic algorithm for learning association rules. Apriori is designed to operate on databases containing transactions. Association rules are the main technique to determine the frequent item set in data mining. It is sometimes referred to as Market Basket Analysis. Market Basket Analysis or Association Analysis is a mathematical modeling technique based upon the theory that if you buy a certain group of items, you are likely to buy another group of items. It is used to analyze the customer purchasing behavior and helps in increasing the sales and maintain inventory by focusing on the point of sale transaction data. It targets customer baskets in order to monitor buying patterns and improve customer satisfaction. By analyzing, recurring patterns in order to offer related goods together can be found and therefore the sales can be increased. Sales on different levels of goods classifications and on different customer segments can be tracked easily. For example, the detection of interesting association relationships between large quantities of business transaction data can assist in catalog design, cross-marketing, lossleader analysis, and various business decision making processes. A typical example of association rule mining is market basket analysis. This method examines customer buying patterns by identifying associations among various items that customers place in their shopping baskets. The identification of such associations can assist retailers expand marketing strategies by gaining insight into which items are frequently purchased jointly by customers. It is helpful to examine the customer purchasing behavior and assists in increasing the sales and conserve inventory by focusing on the point of sale transaction data. This work as a broad area for the researchers to develop a better data mining algorithm. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
|---|
| Classification |
Filipiniana |
| 540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE |
|---|
| Terms governing use and reproduction |
5 |
| 655 ## - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
. |
| Chronological subdivision |
2 |
| 942 ## - ADDED ENTRY ELEMENTS |
|---|
| Institution code [OBSOLETE] |
lcc |
| Item type |
Thesis/Dissertation |
| Source of classification or shelving scheme |
|