Results 41 to 50 of about 238,473 (148)

Privacy Preserving Utility Mining: A Survey

, 2018
In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and ...
Chao, Han-Chieh, Gan, Wensheng, Lin, Jerry Chun-Wei, Wang, Shyue-Liang, Yu, Philip S.   +4 more
core   +1 more source

Event Stream-Based Process Discovery using Abstract Representations [PDF]

, 2017
The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input.
van der Aalst, Wil M. P., van Dongen, Boudewijn F., van Zelst, Sebastiaan J.   +2 more
core   +3 more sources

Fuzzy Sequential Pattern Mining over Quantitative Streams

International Journal of Information and Communication Technology Research, 2019
Sequential pattern mining is an interesting data mining problem with many real-world applications. Though new applications introduce a new form of data called data stream, no study has been reported on mining sequential patterns from the quantitative ...
Omid Shakeri, Manoochehr Kelarestaghi, Farshad Eshghi, Ahmad Ganjtabesh   +3 more
doaj  

Combining value stream mapping and process mining in production: a systematic literature review

Discover Applied Sciences
Value stream mapping is a widely used and proven method in lean production for gaining transparency into the current state of a production process.
Tim Teriete, Klaus Erlach, Thomas Bauernhansl, Wil M. P. van der Aalst   +3 more
doaj   +1 more source

Towards Meta-learning over Data Streams [PDF]

, 2014
Modern society produces vast streams of data. Many stream mining algorithms have been developed to capture general trends in these streams, and make predictions for future observations, but relatively little is known about which algorithms perform ...
Holmes, Geoffrey, Pfahringer, Bernhard, van Rijn, Jan N., Vanschoren, Joaquin   +3 more
core   +1 more source

Caracterización de flujos de datos usando algoritmos de agrupamiento

Tecnura, 2013
This paper presents introductory materials to data-stream mining processes using clustering techniques. The limitations of traditional techniques are observed and the various approaches found in the literature are explained.
Fabián Andrés Giraldo, Elizabeth León, Jonatan Gómez   +2 more
doaj  

Scalable concept drift adaptation for stream data mining

Complex & Intelligent Systems
Stream data mining aims to handle the continuous and ongoing generation of data flows (e.g. weather, stock and traffic data), which often encounters concept drift as time progresses.
Lisha Hu, Wenxiu Li, Yaru Lu, Chunyu Hu
doaj   +1 more source

On Evaluation of Data Stream Clustering Algorithms: A Survey

IEEE Access
Data stream mining is a research area that has grown enormously in recent years. The main challenge is extracting knowledge in real-time from a possibly unbounded data stream.
Christian Nordahl, Veselka Boeva, Hakan Grahn, Marie Persson-Netz   +3 more
doaj   +1 more source

Efficient Approach for Damped Window-Based High Utility Pattern Mining With List Structure

IEEE Access, 2020
Traditional pattern mining is designed to handle binary database that assume all items in the database have same importance, there is a limitation to recognize accurate information from real-world databases using traditional method. To solve this problem,
Hyoju Nam, Unil Yun, Bay Vo, Tin Truong, Zhi-Hong Deng, Eunchul Yoon   +5 more
doaj   +1 more source

Batch-Incremental Learning for Mining Data Streams [PDF]

, 2004
The data stream model for data mining places harsh restrictions on a learning algorithm. First, a model must be induced incrementally. Second, processing time for instances must keep up with their speed of arrival.
Bainbridge, David, Holmes, Geoffrey, Kirkby, Richard Brendon   +2 more
core   +1 more source