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Mining Rich Data Types

Curated by: Huan Liu


The very first issue of data mining and knowledge discovery is to properly handle data. It is essential to take into account different data types. Rich data types can be categorized into: non-dependency and dependency data. The non-dependency data is the most commonly encountered type, which refers to data without specified dependencies between data instances. In other words, data instances are or are assumed independent and identically distributed. Examples of non-dependency data include multidimensional data, text data, and image data. In practice, data can be more complex, and there exists dependency between data instances. Dependency data can be correlated with temporal, spatial, sequential, and social relationships such as time-series, sequence, graph, multi-media, and social-media data.Publications

Non-Dependency Data

1. Text

· Jiawei Han, Heng Ji, and Yizhou Sun. “Successful Data Mining Methods for NLP.” ACL-IJCNLP 2015 (2015). [Tutorial]

2. Image

· Foundations and Trends® in Computer Graphics and Vision, Now Publishers Inc. 2015. http://www.nowpublishers.com/CGV/ [Book chapters]

Dependency Data

3. Time Series Data

o Keogh, Eamonn. “Machine Learning in Time Series Databases (and Everything Is a Time Series.” AAAI’10. http://www.cs.ucr.edu/~eamonn/tutorials.html [Tutorial]

4. Sequence Data

o Mabroukeh, Nizar R., and Christie I. Ezeife. “A taxonomy of sequential pattern mining algorithms.” ACM Computing Surveys (CSUR) 43.1 (2010): 3. [Survey]

5. Dynamic/Streaming Data

o Hans-Peter Kriegel, Irene Ntoutsi, Myra Spiliopoulou, Grigorios Tsoumakas, and Arthur Zimek. “Mining Complex Dynamic Data.” ECML-PKDD 2011. [Tutorial]

6. Graph/Network Data

o Getoor, Lise, and Christopher P. Diehl. “Link Mining: a Survey.” ACM SIGKDD Explorations Newsletter 7.2 (2005): 3-12. [Survey]

o Shamanth Kumar, Fred Morstatter, and Huan Liu. “Analyzing Twitter Data.”Twitter Data Analytics. Springer New York, 2014. 35-48.

7. Social Data

o Mohammad Ali Abbasi, Huan Liu, and Reza Zafarani. Social Media Mining: Fundamental Issues and Challenges. ICDM’13 [Tutorial] http://ecs.syr.edu/faculty/reza/tutorials/ICDM13/TutorialICDM13SMM.pdf

o Jiebo Luo and Tao Mei. Social Multimedia as Sensors. ICDM’14 [Tutorial] http://icdm2014.sfu.ca/program_tutorials.html

8. Spatial and Spatial-Temporal Data

o Aggarwal, Charu C. Chapter 16: Mining Spatial Data, Data mining: The textbook. Springer, 2015. [Book chapter]

9. Multimedia

o Deng, Li, and D. Yu. “Foundations and Trends in Signal Processing.” Signal Processing 7 (2014): 3-4. [Survey]

10. Multi-modularity

o Sun, Shiliang. “A survey of multi-view machine learning.” Neural Computing and Applications 23.7-8 (2013): 2031-2038. [Survey]

Publicly Available Resources

Text Data

o New York Times Annotated Corpus https://catalog.ldc.upenn.edu/LDC2008T19

o 20 Newsgroups Dataset http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/news20.html

o UCI Reuters-21578 Text Categorization Collection

Image Data

o ImageNET http://image-net.org/

Time Series Data

o TREC 2013/2014 Temporal Summarization http://trec.nist.gov/data/tempsumm.html

o UCI Machine Learning Repository (UCI) Synthetic Control Chart Time Series

Sequence Data

o UCI Molecular Biology

Dynamic/Streaming Data

o UCI Synthetic Control Chart Time Series & Pseudo Periodic Synthetic Time Series

Graph/Network Data

o AMiner Citation Network Dataset https://aminer.org/citation

o Stanford Large Network Dataset Collection https://snap.stanford.edu/data/

Social Data

o Social Computing Data Repository at ASU http://socialcomputing.asu.edu/pages/datasets

o MIRFlickr Retrieval Evaluation Dataset http://press.liacs.nl/mirflickr/

Spatial Data

o GDELT Project http://gdeltproject.org/

o UCI Connect-4

Spatio-Temporal Data

o Microsoft Urban Computing Dataset http://research.microsoft.com/en-us/people/yuzheng/#Datasets

o UCI El Nino

Video Data

o TRECVID ’01-’15 http://trecvid.nist.gov/

Audio Data

o Aurora: Timit with noise and additional information http://aurora.hsnr.de/index-2.html

o TIMIT Speech Corpus http://www.ldc.upenn.edu/Catalog/CatalogEntry.jsp?catalogId=LDC93S1

Multi-Modularity Data

o UCSD SVCL Cross Modal Dataset http://www.svcl.ucsd.edu/projects/crossmodal/


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