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- SPMF 2.48 + The Pattern Mining Wiki
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Tag Archives: data science
Brief report about the DSIT 2021 conference (4th Intern. Conf. on Data Science and Information Technology)
This week, I am attending the DSIT 2021 conference (4th International Conference on Data Science and Information Technology) from July 23 to 25 in Shanghai, China. The DSIT 2021 conference is co-located with the DMBD 2021 conference (the 4th International … Continue reading
Posted in artificial intelligence, Big data, Conference, Data Mining
Tagged artificial intelligence, china, data, data mining, data science, dmbd, dsit, machine learning
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A Brief Report about ACIIDS 2021 (13th Asian Conference on Intelligent Information and Database Systems)
In this blog post, I will give a brief report about the ACIIDS 2021 conference, that I am attending from April 7–10, 2021. What is ACIIDS? ACIIDS is an international conference focusing on intelligent information and database systems. The conference … Continue reading
New version of SPMF (2.44): 4 new algorithms, datasets and features
Today, I am happy to announce that a new version of the SPMF open-source data mining software is released (v. 2.44). This is the download page. This new version was made possible due to several contributors. What is new? New … Continue reading
Posted in Data Mining, Data science, open-source, Pattern Mining, Utility Mining
Tagged algorithm, data mining, data science, dataset, open source, open-source, pattern mining, spmf
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Sequential pattern mining vs Sequence prediction ?
In this blog post, I will answer a question that I have received in my e-mail about what is the difference between sequential pattern mining and sequence prediction. I think that this is a good question and sharing the answer … Continue reading
Posted in Big data, Data Mining
Tagged data science, pattern mining, sequence, sequence prediction, sequential pattern
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The 10 most cited papers in our “Data Science and Pattern Recognition” journal!
As some of you know, I am editor-in-chief of the Data Science and Pattern Recognition (DSPR) journal. This journal has started in 2017 and four volumes have been published already with 28 papers, which I consider as a success. One … Continue reading
Posted in Academia, Big data, cfp, Data science, General, Machine Learning
Tagged big data, data mining, data science, dspr, journal, kdd, pattern recognition
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The importance of explainable data science and machine learning models
In this blog post, I will talk about an important concept, which is often overlooked in data mining and machine learning: explainability. To discuss this topic, it is necessary to first remember what is the goal of data mining and machine learning. The goal of data mining is … Continue reading
Five recent books on pattern mining
In this blog post, I will list a few interesting and recent books on the topic of pattern mining (discovering interesting patterns in data). This mainly lists books from the last 5 years. High utility pattern mining: Theory, Applications and algorithms (2019). This … Continue reading
Posted in Big data, Data Mining, Pattern Mining, Utility Mining
Tagged big data, book, data, data mining, data science, pattern mining
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The PAKDD 2020 conference (a brief report)
In this report, I will talk about the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020), from the 11th to 14th May 2020. The PAKDD conference PAKDD is a top international conference on data mining / big data in the Pacific-Asia part of the world. … Continue reading
Posted in Conference, Data Mining, Data science
Tagged conference, data mining, data science, pakdd
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(Video) Sequence prediction with the CPT and CPT+ Models
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 … Continue reading
Correlation does not imply causation
There is a well known principle in statistics that correlation does not imply causation. It means that even if we observe that two variables behave in the same way, we should not conclude that the behavior of one of those variables … Continue reading
Posted in Big data, Data Mining, Data science
Tagged causation, correlation, data, data analysis, data mining, data science
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