000			02795cam a2200313 a 4500
003			EG-GiCUC
008			190716s2018 ua d f m 000 0 eng d
040			_aEG-GiCUC _beng _cEG-GiCUC
041	0		_aeng
049			_aDeposite
097			_aM.Sc
099			_aCai01.18.02.M.Sc.2018.Eb.I
100	0		_aEbtesam Elhossiny Elhossiny Hassan Shemis
245	1	3	_aAn improved approach for association rule extraction using fuzzy formal concept analysis / _cEbtesam Elhossiny Elhossiny Hassan Shemis ; Supervised Hesham Ahmed Hefny , Ahmed Mohammed Gadallah
246	1	5	_aأسلوب مُحسُّن لاستخلاص قواعد الارتباط باستخدام تحليل المفهوم الصوري الفازي
260			_aCairo : _bEbtesam Elhossiny Elhossiny Hassan Shemis , _c2018
300			_a199 Leaves : _bcharts ; _c30cm
502			_aThesis (M.Sc.) - Cairo University - Institute of Statistical Studies and Research - Department of Computer and Information Science
520			_aIntuitively, extensive involvement of the computerized system in almost all life aspects produces a massive amount of data. Such data needs to be processed, analyzed, retrieved and mined. Formal concept analysis (FCA) is a powerful tool for handling data manipulation tasks (retrieval, analysis, and mining). However, the classical FCA can only handle binary data directly. Therefore, it handles quantitative data by mapping them to binary values through dividing attribute range into a set of disjoint intervals. In consequence, FCA suffers from crisp boundaries problem regarding quantitative datasets. One of the most promising ways to overcome such deficiency in FCA is the adoption of the fuzzy set and fuzzy logic theory. In this case, fuzzy FCA (FFCA) can easily deal with such clear-cut boundaries in quantitative datasets. This dissertation concerned mainly with the ability to perform data mining in large data sets with the aid of FFCA. The proposed approach introduces the notion of one-sided fuzzy iceberg lattice which accelerates the entire mining process and best suits the association rule mining approach. The proposed fuzzy iceberg lattice contains all frequent closed itemsets associated with their corresponding fuzzy supports. Hence, it presents a straight-forward way for extracting association rules. Furthermore, in this dissertation, two enhanced algorithms, object-based and attribute-based, for extracting fuzzy concepts using FFCA are proposed
530			_aIssued also as CD
653		4	_aFormal concept analysis (FCA)
653		4	_aFuzzy FCA (FFCA)
653		4	_aMining
700	0		_aAhmed Mohammed Gadallah , _eSupervisor
700	0		_aHesham Ahmed Hefny , _eSupervisor
905			_aNazla _eRevisor
905			_aShimaa _eCataloger
942			_2ddc _cTH
999			_c72903 _d72903