KLASIFIKASI SECARA EFISIEN PADA DATABASE MULTI RELASI DENGAN ALGORITMA CROSSMINE
Abstract: Multi-relation
classifications can be widely used in many disciplines, such as financial
decision making, medical research, and geographical applications, and
information stored in multiple relations needs to be used in decision making.
Crossmine is an efficient and scalable approach for multi-relation
classification. Crossmine algoritm has three step, first is find-rules, the
rule has been gotten from find a rule process than remove all positif tuples
satisfying rule while there are more than ten percent positif tuple left. The
second is find a rule, this step has input from the result of find best
predicate process, that is the complex predicate with most foilgain. If
foilgain value is more than mingain, the predicate is added with rule, and max
rule length less than six. Third is find best predicate, in this step we find
the best predicate with definition, if the foilgain value more than the max
gain value, the predicate will be saved and the bigger gain value will replace
the last gain value for next comperative process. In other side, the accuracy
is computed from each rule that produce in find rules process. The test for
this application use the sum tuple of 200, 500, 1000, 5000 for measuring the
level of accuracy from rule which is produced by crossmine algoritm.
Penulis: Sarwosri, Darlis
Herumurti, Indri Sulistyowati
Kode Jurnal: jptkomputerdd080027
