Mining Top-k Frequent-regular Itemsets from Data Streams Based on Sliding Window Technique
Tashinee Mesama, Komate Amphawan · 2018
Frequent-regular itemset mining has achieved a great attention and applied in several applications. In this framework, an itemset that frequently and regularly occurs in a database is identified as interesting. However, without prior knowledge, the setting appropriate support and regularity thresholds to measure interestingness of itemsets is quite difficult. This may lead to none, only few or overwhelm of generated results causing users cannot further take advantages from these itemsets. In addition, mining interesting itemsets over data streams is a challenging task on various domains. Therefore, to cope with these issues, we here propose an approach to mine top-k frequent-regular itemsets over data streams. To mine such itemsets, the concept of sliding window is applied in which recent occurrences are considered to be more important than the former occurrences. An efficient single-pass algorithm, called TFRIM-DS, is also introduced to mine a set of k itemsets that regularly occur and have highest support in the current considered window. In addition, a bit-vector with a reuse technique is applied and designed to efficiently maintain occurrence information of each itemset. Experiments were conducted and showed efficiency of our proposed TFRIM-DS to mine top-k frequent-regular itemsets over sliding window of data streams.