الفهرس 
Data Mining Standard FeaturesOnly 14 pages are availabe for public view
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Abstract
This thesis presents a new approach for Incremental Association Rules Mining (IARM).
The developed algorithm are called SHTA and DHTA, which based on the Apriori algorithm.
The two algorithms use the old dataset (OD) only once and utilized the power of data structure by applying the static hashing table that adopted in SHTA algorithm and the dynamic hashing table that adopted in DHTA algorithm. DHTA algorithm utilized to overcome the drawbacks in SHTA algorithm such that the building of static hash table depends only the number of items but the building of dynamic hash table depends on the size of items, and the size of dataset.
The achieved results reveal that the new approach performs much better in the execution time with large datasets.
The proposed approach has several additional saving features; the updating process updates the existing dynamic hash table with no need to build a new one, it is actually not hardware dependent and the algorithm permits the end user to change the user threshold value and confidence factor without re-scanning the dataset.