Tag Archives: data science

Analyzing the source code of SPMF (5 years later)

Posted on 2019-01-12 by Philippe Fournier-Viger

Five years ago, I had analyzed the source code of the SPMF data mining software using an open-source tool called CodeAnalyzer ( http://sourceforge.net/projects/codeanalyze-gpl/ ). This had provided some interesting insights about the structure of the project, especially in terms of lines of codes and code to … Continue reading →

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(video) Mining Sequential Rules with RuleGrowth

Posted on 2018-12-26 by Philippe Fournier-Viger

This is a video presentation of the paper “Mining Partially-Ordered Sequential Rules Common to Multiple Sequences” about discovering sequential rules in sequences using the RuleGrowth algorithm. VIDEO LINK: https://www.philippe-fournier-viger.com/spmf/videos/rulegrowth.mp4 More information about the RuleGrowth algorithm are provided in this research paper: Fournier-Viger, P., Wu, C.-W., Tseng, V.S., Cao, L., Nkambou, R. (2015). Mining Partially-Ordered Sequential Rules Common to Multiple … Continue reading →

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(video) Minimal Correlated High Utility Itemsets with FCHM

Posted on 2018-12-20 by Philippe Fournier-Viger

This is a video presentation of the paper “Mining Correlated High-Utility Itemsets Using the bond Measure” about correlated high utility pattern mining using FCHM.  VIDEO LINK: https://www.philippe-fournier-viger.com/spmf/videos/FCHM_correlated_itemsets.mp4 More information about the FCHM algorithm are provided in this research paper: Fournier-Viger, P., Zhang, Y., Lin, J. C.-W., … Continue reading →

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Introduction to frequent subranking mining

Posted on 2018-12-13 by Philippe Fournier-Viger

Rankings are made in many fields, as we naturally tend to rank objects, persons or things, in different contexts. For example, in a singing or a sport competition, some judges will rank participants from worst to best and give prizes to … Continue reading →

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Skills needed for a data scientists? (comments on the HBR article)

Posted on 2018-10-26 by Philippe Fournier-Viger

Recently, I have read an article of the Harvard Business Review (HBR) website about data sciences skills for businesses. This article proposes to categorize skills related to data on a 2×2 matrix where skills are labelled as useful VS not useful, and … Continue reading →

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Report about the DEXA 2018 and DAWAK 2018 conferences

Posted on 2018-09-06 by Philippe Fournier-Viger

This week, I am attending the DEXA 2018 (29th International Conference on Database and Expert Systems Applications) and the DAWAK 2018 (20th Intern. Conf. on Data Warehousing and Knowledge Discovery) conferences from the 3rd to 6th September in Regensburg, Germany. Those two conferences are well established European conferences dedicated mainly to research on database and data mining. These conferences are always collocated. … Continue reading →

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Report about the KDD 2018 conference

Posted on 2018-08-20 by Philippe Fournier-Viger

This week, I am participating to the KDD 2018 ( 24th ACM SIGKDD Intern. Conference on Knowledge Discovery and Data Mining), in London, UK from the 19th to 23rd August 2018. The KDD conference is an international conference, established 24 years ago. It is the top conference in the field of data mining / … Continue reading →

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The future of pattern mining

Posted on 2018-08-10 by Philippe Fournier-Viger

In this blog post, I will talk about the future of research on pattern mining. I will also discuss some lessons learnt from the decades of research in this field and talk about research opportunities. What is the state of research on pattern mining? Over the last … Continue reading →

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The PAKDD 2017 conference (a brief report)

Posted on 2017-05-26 by Philippe Fournier-Viger

This week, I have attended the PAKDD 2017 conference in Jeju Island, South Korea, this week, from the 23 to 26th May.  PAKDD is the top data mining conference for the asia-pacific region. It is held every year in a different pacific-asian country. In this blog post, I will write a brief report about … Continue reading →

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This is why you should visualize your data!

Posted on 2017-03-11 by Philippe Fournier-Viger

In the data science and data mining communities, several practitioners are applying various algorithms on data, without attempting to visualize the data.  This is a big mistake because sometimes, visualizing the data greatly helps to understand the data. Some phenomena are obvious when visualizing the data. In this blog post, I will give a few … Continue reading →

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