@inproceedings{b23efc252bc2466cac60b1eebe0b68da,
title = "The Partial-Value Association Discovery Algorithm and Applications",
abstract = "Existing techniques for machine learning and data mining have shortcomings in handling data of different types, data without a priori knowledge of data dependence, and data with variable relations varying in different value ranges. This paper illustrates how the Partial-Value Association Discovery (PVAD) algorithm overcomes shortcomings of existing machine learning and data mining techniques. The paper also demonstrates how the PVAD algorithm was used to analyze engineering student data and computer network data for identifying characteristics of engineering retention and network traffic normalcy.",
keywords = "Data association, Data mining, Engineering retention, Machine learning, Network traffic analysis",
author = "Nong Ye and Fok, {Ting Yan} and James Collofello and Douglas Montgomery and Kevin Mills",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 Amity International Conference on Artificial Intelligence, AICAI 2019 ; Conference date: 04-02-2019 Through 06-02-2019",
year = "2019",
month = apr,
day = "26",
doi = "10.1109/AICAI.2019.8701378",
language = "English (US)",
series = "Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6--13",
booktitle = "Proceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019",
}