KDD honors Yiming Ma and Jaewon Yang

Every year researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data come together at KDD, the #1 data mining conference in the world. KDD is an interdisciplinary conference that brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data. This year two members of our data science and relevance team are being honored at KDD 2014 for their work. Yiming Ma received the test of time paper award and Jaewon Yang won an honorable mention for his thesis work.

Yiming Ma has won the SIGKDD 2014 Test of Time award for his contributions to the research paper “Integrating Classification and Association Rule Mining” that was published in KDD 1998.

This is the first time that SIGKDD has offered this award. The award committee is composed of well-known researchers in data mining and this selection reflects the significant impact of Yiming's research. His paper pioneered the research of using association rules for classification by integrating classification and association rule mining. It also proposed an efficient algorithm and built the first system (called CBA) for the purpose. This work triggered a large number of follow-up works and numerous applications. In addition to classification, due to its ability to generate all rules, it enables the user to understand the data and to find causes of problems and actionable knowledge to solve the problems. Such diagnostic data mining is crucial for many engineering applications, but it is hard to do with other classification methods because they produce only a small model sufficient for classification while missing out a large number of regularities in data.

Jaewon Yang has won the SIGKDD doctoral dissertation award honorable mention for his dissertation “Community Structure of Large Networks”.

The SIGKDD doctoral dissertation award is the most prestigious award recognizing the best doctoral candidates in the field of data mining in each year. Jaewon’s thesis developed scalable and accurate methods for detecting network communities (dense node groups) in large-scale networks ranging from social networks to biological networks. Jaewon’s methods advance the state-of-the-art by an order of magnitude both in accuracy and scalability.

Congratulations Yiming and Jaewon!