Abstract
In recent times, mining of frequent pattern in progressive databases is a very attractive area of research. In real world applications such as market basket analysis of retail-shop where the items are associated static attribute weight, which reflects each item has different importance and dynamic attribute support, which represents the frequency of an item. The mining of items which is having both static and dynamic attributes reveals an important knowledge than the traditional patterns. We use two notions in the process of mapping input items to general tree structure. One, the product of dynamic attribute value support and static attribute weight should be greater than user defined threshold. Second, the dynamic attribute value support should be greater than user defined threshold. Our proposed approach uses sliding window and apriori’s antimonotone principle in mining the items associated weight and/or support over progressive databases.
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Keshavamurthy, B.N., Sharma, M., Toshniwal, D. (2012). Efficient Mining of Frequent Items Coupled with Weight and /or Support over Progressive Databases. In: Kannan, R., Andres, F. (eds) Data Engineering and Management. ICDEM 2010. Lecture Notes in Computer Science, vol 6411. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27872-3_46
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DOI: https://doi.org/10.1007/978-3-642-27872-3_46
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