Data Mining and Knowledge Discovery (DMKD, since 1997)
Knowledge and Information Systems (KAIS, since 1999)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
Many others, incl. TPAMI, ML, IDA, …
IEEE ISI-2005 Panel on Technical ISI Research, May 19, 2005 ACM KDD vs. IEEE ICDM
TKDE Topics Related to ISI
Database and Data Modeling
Knowledge Engineering and Intelligent Systems
social networks and graph analysis
Main Topics in Data Mining
Association analysis (frequent patterns)
Classification (trees, Bayesian methods, etc)
Clustering and outlier analysis
Sequential and spatial patterns, and time-series analysis
Text and Web mining
Data visualization and visual data mining.
Fundamental Knowledge Engineering/AI Techniques in Data Mining
Knowledge representation . Data mining seeks to discover interesting patterns from large volumes of data. These patterns can take various forms, such as association rules, classification rules, and decision trees.
Knowledge acquisition . The discovery process shares various heuristic algorithms and methods with machine learning for the same purpose of knowledge acquisition from data or learning from examples.
Knowledge inference . The patterns discovered from data need to be verified in various applications so deduction of mining results is an essential technique in data mining applications.
Some Research Directions
Web mining (incl. Web structures, usage analysis, authoritative pages, and document classification)
Intelligent data analysis in domain-specific applications (such as bioinformatics and ISI)
Mining with data streams (in continuous, real-time, dynamic data environments)
Integrated, intelligent data mining environments and tools (incl. induction, deduction, and heuristic computation).
How to Publish ISI Research at ICDM and TKDE?
ICDM and TKDE both look for technological contributions
ICDM and TKDE are both very tough, expecting best results in their respective research field
Reading and citing relevant papers from ICDM/KDD and TKDE is a must
A possible way to publish in both ICDM/KDD and TKDE:
Submit to ICDM/KDD to get (quick) feedback
Expand and submit to TKDE if positive feedback from ICDM/KDD, with at least 30% new material.
How to Publish ISI Research at ICDM and TKDE (2)
How about ISI application papers?
Application papers are always invited, but innovations are necessary. A case of an innovative application must be presented, for the ICDM/TKDE audience.
How about data analysis w/o large volumes of data?
Experiments on large databases are not always required, but relevance to mining/discovery must be established.
Most important of all: the uniqueness of your research in the field!
You work has to be (1) technically sound, (2) relevant, (3) original, (4) significant, and (5) well clarified.
ISI research needs knowledge and data engineering systems and tools.
Data mining conferences (such as KDD and ICDM) are good forums to publish ISI research, but they expect technological contributions and/or innovative applications.
TKDE publishes “well-defined theoretical results and empirical studies that have potential impact on the acquisition, management, storage, and graceful degeneration of knowledge and data, as well as in provision of knowledge and data services.”