Samaher Al-Janabi - Department of Computer Science, Faculty of Science for Women (SCIW), University of Babylon, Babylon, Iraq
Ibrahim Al-Shourbaji - Computer Network Department, Computer Science and Information System College University of Jazan, Saudi Arabia

In this paper, a novel method named as Frequency Pattern-Knowledge Constructions (FP-KC) is developed. This method attempts to develop Frequency Pattern (FP) Growth data mining algorithm using several knowledge constructions to find the association rules and minimize the shared information ( i.e. fined frequent item set), FP-KC combines the criteria of Principal Component Analysis (PCA) with FP-Growth techniques. These criteria include eigenvalues, cumulative variability and scree plot. There are several reasons for developing the FP-Growth data mining algorithm to build up a novel FP-KC technique that can find the association rules, including: (a) the size of an FP-tree is typically smaller than the size of the uncompressed data because many records in a dataset often have a few items (b) to give the best result in the case that all the records have the same set of items; (c) FP-Growth is an efficient algorithm because it illustrates how a compact representation of the transaction dataset helps to efficiently generate frequent item sets; and (d) The run-time performance of FP-Growth depends on the compaction factor of the dataset, while the enhanced algorithm in Subliminal Channel (SC) depends on both the position of a character in the alphabet and its position in the plain rule word (i.e. rules resulting from association rules FP-KC), with a specific function to determine the cipher rule character. To evaluate the efficiency of the proposed method, four case studies were used. Based on the results, the proposed method can be considered as an efficient technique for secure mining of association rules of partitioned data compared with the traditional method.

Data Mining – Subliminal Cryptography – Association Rules ,Constructions, Principle Component Analysis