Today, I presents the **CPT **and **CPT+** sequence prediction models in a video. **Sequence prediction** is an important task in data mining which consists of predicting the next symbols of a sequence. It can be used for example to predict the next word that someone will type on a keyboard, or the next location where someone will go.

The official implementations of **CPT and CPT+ models **and **datasets** for evaluating their performance are available in the SPMF software, which is implemented in **Java **and open-source. There is also an unofficial implementation of CPT in **Cython**.

The **CPT+** (Compact Prediction Tree+) model is described in this article:

*Gueniche, T., Fournier-Viger, P., Raman, R., Tseng, V. S. (2015). CPT+: Decreasing the time/space complexity of the Compact Prediction Tree. Proc. 19th Pacific-Asia Conf. Knowledge Discovery and Data Mining (PAKDD 2015), Springer, LNAI9078, pp. 625-636.*

The original CPT algorithm was described in this paper:

*Gueniche, T., Fournier-Viger, P., Tseng, V. S. (2013). Compact Prediction Tree: A Lossless Model for Accurate Sequence Prediction. Proc. 9th Intern. Conference on Advanced Data Mining and Applications (ADMA 2013) Part II, Springer LNAI 8347, pp. 177-188.*

That is all for today. More data mining videos will be posted soon!

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Philippe Fournier-Viger is a professor, data mining researcher and the founder of the SPMF data mining software, which includes more than 150 algorithms for pattern mining.