Mining Effective Subsequences with Application in Marketing Attribution
| Autor: | Zi Yin, Pietro Mazzoleni, Yuanyuan Shen, Ying Li |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Sequence
Focus (computing) Computer science business.industry 02 engineering and technology Machine learning computer.software_genre Data modeling 03 medical and health sciences 0302 clinical medicine Order (business) 030220 oncology & carcinogenesis Subsequence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Algorithm design Artificial intelligence Data mining Marketing Attribution business computer |
| Zdroj: | ICDM Workshops |
| DOI: | 10.1109/icdmw.2016.0104 |
| Popis: | In this paper, we present a new data mining framework for discovering sequence effects. In particular, we focus on the sequences consisting of actions that are taken in chronological order, like sequences of clinical procedures or marketing actions. Each sequence is associated with a binary outcome, a success or a failure. We investigate the hypothesis that certain subsequences of actions contribute to successes, which we call effective subsequences. A generic data mining algorithm for extracting effective subsequences is proposed, which is verified both quantitatively and qualitatively. We experimented our effective subsequence mining algorithm on a real sales opportunity dataset. Based on the subsequence model, a market campaign attribution model is proposed, with application to the same sales dataset. |
| Databáze: | OpenAIRE |
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