One of the major challenges for modern technology companies is user retentionmanagement. This work focuses on identifying early usage patterns that signifyincreased retention rates in a mobile web browser.This is done using a targetedparallel implementation of the association rule mining algorithm FP-Growth.Different item subset selection techniques including clustering and otherstatistical methods have been used in order to reduce the mining time and allowfor lower support thresholds.A lot of interesting rules have been mined. The best retention-wise ruleimplies a retention rate of 99.5%. The majority of the rules analyzed in thiswork implies a retention rate increase between 150% and 200%.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-137793 |
| Date | January 2017 |
| Creators | Persson, Pontus |
| Publisher | Linköpings universitet, Databas och informationsteknik |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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