id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_3gmfusebffdg3m6qdqgyme5u7e	F THABTAH	Mining the data from a hyperheuristic approach using associative classification	2008	10	.pdf	application/pdf	7113	634	64	rule inducers in solutions (data sets) produced by a general-purpose optimisation heuristic called the hyperheuristic for a personnel After experimenting 16 different solution generated by a hyperheuristic called Peckish using different classification approaches, the results indicated that associative classification approach is the most applicable approach to such kind of problems selection of low-level heuristics from the data sets more accurately than C4.5, RIPPER and PART algorithms, respectively. The arrows going from and to the hyperheuristyic in Fig. 1 represent the selected low-level heuristics improvement values on the objective function In the next section, we investigate some of the popular data mining techniques for learning the sets of low-level heuristics that improve the The MMAC algorithm consists of three steps: rules generation, recursive learning and classification. C4.5 algorithm was created by Quinlan (1993) as a decision tree method for extracting rules from a data set.	./cache/work_3gmfusebffdg3m6qdqgyme5u7e.pdf	./txt/work_3gmfusebffdg3m6qdqgyme5u7e.txt