TY  - JOUR
T1  - Bilgi Keşfi Sürecinde Bayesci Ağlar ve Birliktelik Analizi
TT  - Bayesian Networks and Association Analysis in Knowledge Discovery Process
AU  - Ersel, Derya
AU  - Günay, Süleyman
PY  - 2012
DA  - June
JF  - İstatistikçiler Dergisi:İstatistik ve Aktüerya
JO  - JSSA
PB  - Aktüerya Derneği
WT  - DergiPark
SN  - 1308-0539
SP  - 51
EP  - 64
VL  - 5
IS  - 2
LA  - tr
AB  - Veri madenciliği, büyük veri kümelerinden yararlı bilginin, bilinmeyen örüntülerin ve ilginç ilişkilerin ortaya çıkartıldığı istatistiksel bir süreçtir. Bu süreçte, pek çok istatistiksel yöntem kullanılabilir. Bu yöntemlerden ikisi Bayesci ağlar ve birliktelik analizidir. Bayesci ağlar, bir veri tabanında yer alan raslantı değişkenlerinin bir kümesindeki olasılıksal ilişkileri kodlayan grafiksel modellerdir. Hem nedensel hem de olasılıksal özelliklere sahip olduğundan Bayesci ağlar ile veri ve uzman bilgisi kolaylıkla birleştirilebilir. Bayesci ağlar ayrıca, ilgilenilen problemin kesin olmayan tanım kümesi hakkındaki bilgiyi temsil etmek için kullanılır ve güçlü çıkarsamaların yapılmasını sağlar. Birliktelik analizi, büyük veri tabanlarındaki gizli birlikteliklerin, yararlı kuralların ve şaşırtıcı örüntülerin ortaya çıkartılmasını sağlayan bir yöntemdir.Birliktelik analizinin bir kusuru, veri kümesi çok küçük olsa dahi çok sayıda örüntünün ortaya çıkartılmasıdır.Bu nedenle, bu örüntülerden ilginç olmayanların elenmesi için ilginçlik ölçümleri kullanılmalıdır
KW  - bayesci ağlar
KW  - birliktelik analizi
KW  - ilginçlik ölçümleri
KW  - sık gözlenen nesne kümeler
N2  - Datamining is a statistical process to extract useful information, unknown patternsand interesting relationships in large databases. In this process, manystatistical methods are used. Two of these methods are Bayesian networks andassociation analysis. Bayesian networks are probabilistic graphical models thatencode relationships among a set of random variables in a database. Since theyhave both causal and probabilistic aspects, data information and expertknowledge can easily be combined by them. Bayesian networks can also representknowledge about uncertain domain and make strong inferences. Associationanalysis is a useful technique to detect hidden associations and rules in largedatabases, and it extracts previously unknown and surprising patterns fromalready known information. A drawback of association analysis is that manypatterns are generated even if the data set is very small. Hence, suitableinterestingnes measures must be performed to eliminate uninteresting patterns. Bayesian networks and associationanalysis can be used together in knowledge discovery. As association rules areused to create Bayesian networks, interestingness measures to determineinteresting patterns can be established by Bayesian networks. In this study,this mutual utilization between Bayesian Netwoks and association analysis isexplained and an illustration over a real life problem is presented.
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UR  - https://dergipark.org.tr/en/pub/jssa/issue//123891
L1  - https://dergipark.org.tr/en/download/article-file/105678
ER  -