2007/05/08 by Edgar de Graaf Joost Kok Walter Kosters, Kosters, Edgar de Graaf Joost Kok Walter
Computer Science · #Advanced Database Systems and Queries #Data Mining Algorithms and Applications #Time Series Analysis and Forecasting #cs.AI #cs.DB
paper · pdf · doi:10.48550/arxiv.0705.1110
arxiv created 2007/05/08 · arxiv updated 2009/12/01
In many applications it will be useful to know those patterns that occur with a balanced interval, e.g., a certain combination of phone numbers are called almost every Friday or a group of products are sold a lot on Tuesday and Thursday. In previous work we proposed a new measure of support (the number of occurrences of a pattern in a dataset), where we count the number of times a pattern occurs (nearly) in the middle between two other occurrences. If the number of non-occurrences between two occurrences of a pattern stays almost the same then we call the pattern balanced. It was noticed that some very frequent patterns obviously also occur with a balanced interval, meaning in every transaction. However more interesting patterns might occur, e.g., every three transactions. Here we discuss a solution using standard deviation and average. Furthermore we propose a simpler approach for pruning patterns with a balanced interval, making estimating the pruning threshold more intuitive.