Mining Patterns with a Balanced Interval

@misc{citeulike:1286089, abstract = {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.}, author = {Edgar}, citeulike-article-id = {1286089}, eprint = {0705.1110}, keywords = {data, mining, patterns}, month = {May}, posted-at = {2007-05-09 19:22:01}, priority = {2}, title = {Mining Patterns with a Balanced Interval}, url = {http://arxiv.org/abs/0705.1110}, year = {2007} }

TY - ELEC ID - citeulike:1286089 L3 - citeulike-article-id:1286089 TI - Mining Patterns with a Balanced Interval N2 - 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. KW - data KW - mining KW - patterns AU - Edgar PY - 2007/May/08/ UR - http://arxiv.org/abs/0705.1110 ER -