000			03825nam a2200301Ia 4500
001			74116
003			ft6076
005			20251124103507.0
008			180920n 000 0 eng d
040			_erda
041			_aengtag
050			_aQA76.9 M35 2016
082			_a.
100			_aMalubag, Zia Yzabelle G.; Navarrete, Shiela Marie M.
245		0	_aAn Enhancement of the Equivalence Class Transformation Algorithm Applied in Market Basket Analysis
264			_a. _b. _c2016
300			_bUndergraduate Thesis: (BSCS major in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2016.
336			_b. _atext _2rdacontent
337			_30 _b. _aunmediated _2rdamedia
338			_30 _b. _avolume _2rdacarrier
505			_aABSTRACT: Data mining has a major concern on precise specification on items in large databases. The equivalence class transformation algorithm is specifically applied in market basket analysis. Market Basket Analysis is used to know the statistics of items that are less salable and frequently sold. However, the researchers found some problems and limitations in the existing process. Through simulation of the existing algorithm the researchers found out that for having bigger Transaction ID set the computation time is costly when interesting. Along with this, due to its downward closure property, the infrequent items set often found on the later part of candidate generation. Lastly, through the use of uniform user-defined support count the number if item sets to be prune is numerous. Due to the slow performance in analysing huge volumes of data, the researchers came up with simplified process to a faster and more convenient transaction that will benefit both the consumers and dealers. The researchers introduce an enhancement to the existing process of the equivalence class transformation wherein it lessens the number of transaction to be process. The infrequent items can be easily distinguished and the upgrade of the use of support count is implemented. The enhancement of equivalence class transformation algorithm presented by the researchers is beneficial for easy, fast, and convenient transaction of items that will help both consumers and dealers.
506			_a5
520			_aABSTRACT: Data mining has a major concern on precise specification on items in large databases. The equivalence class transformation algorithm is specifically applied in market basket analysis. Market Basket Analysis is used to know the statistics of items that are less salable and frequently sold. However, the researchers found some problems and limitations in the existing process. Through simulation of the existing algorithm the researchers found out that for having bigger Transaction ID set the computation time is costly when interesting. Along with this, due to its downward closure property, the infrequent items set often found on the later part of candidate generation. Lastly, through the use of uniform user-defined support count the number if item sets to be prune is numerous. Due to the slow performance in analysing huge volumes of data, the researchers came up with simplified process to a faster and more convenient transaction that will benefit both the consumers and dealers. The researchers introduce an enhancement to the existing process of the equivalence class transformation wherein it lessens the number of transaction to be process. The infrequent items can be easily distinguished and the upgrade of the use of support count is implemented. The enhancement of equivalence class transformation algorithm presented by the researchers is beneficial for easy, fast, and convenient transaction of items that will help both consumers and dealers.
526			_aF
540			_a5
655			_aacademic writing
942			_alcc _cARCHIVES _2lcc
999			_c25391 _d25391