(video) Minimal Periodic Frequent Itemset with PFPM

This is a video presentation of the paper “PFPM: Discovering Periodic Frequent Patterns with Novel Periodicity Measures” about periodic pattern mining using PFPM. It is part of my new series of videos about data mining algorithms  (link to download the … Continue reading

(video) Minimal High Utility Itemset Mining with MinFHM

This is a video presentation of the paper “Mining Minimal High Utility Itemsets” about high utility itemset mining using MinFHM. It is the first video of a series of videos that will explain various data mining algorithms. (link to download … Continue reading

Upcoming book: High Utility Itemset Mining: Theory, Algorithms and Applications

I am happy to announce that the draft of the book about high utility pattern mining has been finalized and submitted to the publisher (Springer). It should thus be published in the very near future. The book contains 12 chapters written … Continue reading

Report about the DEXA 2018 and DAWAK 2018 conferences

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. … Continue reading

Report about the KDD 2018 conference

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 … Continue reading

A Model for Football Pass Prediction (source code + dataset)

In this blog post, I will discuss the data challenge of the Machine Learning for Sport Analytics workshop (MLSA 2018) at PKDD 2018. The challenge consisted of predicting the receivers of football passes (pass prediction). I will first briefly describe … Continue reading

PAKDD 2018 Conference (a brief report)

In this blog post, I will discuss the PAKDD 2018 conference (Pacific Asia Conference on Knowledge Discovery and Data Mining), in Melbourne Australia, from the 3rd June to the 6th June 2018. About the PAKDD conference PAKDD is an important conference … Continue reading


哈尔滨工业大学(深圳)工业设计研究中心正在招聘两名博士后研究人员进行数据挖掘/大数据方向的研究。 招聘条件: 计算机科学博士学位, 在数据挖掘或人工智能领域有着深厚的研究背景, 在数据挖掘或人工智能领域的优秀会议或期刊上发表过论文, 对数据挖掘算法的开发和应用有浓厚兴趣, 211/ 985大学或国外优秀学校博士学位优先考虑。 成功申请人将: 工作在与时间序列和空间序列相关方面或者其它与数据挖掘领域相关的理论或者工业应用。(确切的主题会根据申请人的优势讨论后确定)。 加入由Philippe Fournier-Viger教授领导的优秀研究团队,Philippe Fournier-Viger教授是流行数据挖掘库SPMF的创始人,并且与其他领域的优秀研究人员有密切合作。 工作在具有先进设备的实验室(实验室配备高端的工作站,用于大数据研究的服务器集群,GPU服务器,虚拟现实设备,身体传感器等)。 以年薪17.6万元人民币聘用两年(其中51600来自学校,120,000来自深圳市政府)。请注意,博士后研究员不需要对工资支付任何税费,学校会提供低价格的租赁公寓(大约1500/月,很大地节省了住宿费用)。 工作在全球计算机科学领域排名前50的大学之一,以及中国排名前10的大学之一。 工作在中国东南部增长最快的城市之一深圳,这里污染低,全年气候温暖,接近香港。 如果您对此职位感兴趣,请尽快发送您的详细简历(包括出版物和参考文献清单)至Philippe Fournier-Viger教授(philfv8@yahoo.com ),可以申请2018年或2019年的博士后名额。 Related posts:Conference reviewers procrastinate?China lead in mobile payment and services10 ways of becoming more efficient at doing research … Continue reading