Bitwise dynamic itemset counting algorithm

Data mining has gained a lot of importance as well as popularity in today´s world. Data mining provides a systematic approach for gathering useful information from huge amounts of data. Many algorithms are being written for this purpose. One of them is Dynamic Itemset Counting Algorithm. Only if all the subsets are frequent, an itemset is considered frequent in this algorithm. As the itemsets are counted, they are grouped together into four separate categories namely, dashed circle, dashed box, solid circle, and solid box. Here, a variation of this existing algorithm is being provided. Bitwise Dynamic Itemset Counting Algorithm aims to modify the existing algorithm such that its time complexity reduces. In today´s world, it is very important not only to collect information from raw data but also to do it fast. Time required for running any algorithm on a collection of data directly impacts the usefulness of that algorithm. Hence, reduction of the time complexity of an existing data mining algorithm such as Dynamic Itemset Counting Algorithm shall be useful. In the existing algorithm, all transactions are checked during every pass for detecting the frequency of the different itemsets. The modified algorithm attempts to suggest a more efficient way to achieve the same results. It also aims at reducing the number of comparisons and the required number of scans. Bitwise Dynamic Itemset Counting Algorithm uses bitwise mapping of all transactions corresponding to each distinct item and the possibility check.