Class Association Rule Pada Metode Associative Classification
Abstract: Frequent patterns
(itemsets) discovery is an important problem in associative classification rule
mining. Differents approaches have been
proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and
Transaction Data Location (Tid)-list Intersection algorithm. This paper focuses
on surveying and comparing the state of the art associative classification
techniques with regards to the rule generation phase of associative
classification algorithms. This phase
includes frequent itemsets discovery and rules mining/extracting methods to
generate the set of class association rules (CARs). There are some techniques proposed to improve
the rule generation method. A technique
by utilizing the concepts of discriminative power of itemsets can reduce the
size of frequent itemset. It can prune
the useless frequent itemsets. The closed frequent itemset concept can be
utilized to compress the rules to be compact rules. This technique may reduce the size of
generated rules. Other technique is in
determining the support threshold value of the itemset. Specifying not single
but multiple support threshold values with regard to the class label
frequencies can give more appropriate support threshold value. This technique may generate more accurate
rules. Alternative technique to generate rule is utilizing the vertical layout
to represent dataset. This method is
very effective because it only needs one scan over dataset, compare with other
techniques that need multiple scan over dataset. However, one problem with these approaches
is that the initial set of tid-lists may be too large to fit into main memory.
It requires more sophisticated techniques to compress the tid-lists.
Penulis: Eka Karyawati, Edi
Winarko
Kode Jurnal: jptinformatikadd110169
