An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
The use of MMR, diversity-based reranking for reordering documents and producing summaries
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Upper semi-lattice of binary strings with the relation "x is simple conditional to y"
Theoretical Computer Science
Conditional complexity and codes
Theoretical Computer Science
Logical operations and Kolmogorov complexity
Theoretical Computer Science
Independent minimum length programs to translate between given strings
Theoretical Computer Science
Information distance and conditional complexities
Theoretical Computer Science
Complex Data: Mining Using Patterns
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Logical Operations and Kolmogorov Complexity II
CCC '01 Proceedings of the 16th Annual Conference on Computational Complexity
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Movie Review Mining: a Comparison between Supervised and Unsupervised Classification Approaches
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 4 - Volume 04
Algorithmic Clustering of Music Based on String Compression
Computer Music Journal
Clustering Fetal Heart Rate Tracings by Compression
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Movie review mining and summarization
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Extracting product features and opinions from reviews
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Information distance from a question to an answer
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Pulse: mining customer opinions from free text
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
IEEE Transactions on Information Theory
Shared information and program plagiarism detection
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Specialized Review Selection for Feature Rating Estimation
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Compressing lists for audio classification
Proceedings of 3rd international workshop on Machine learning and music
A review selection approach for accurate feature rating estimation
COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics: Posters
A new approach for multi-document update summarization
Journal of Computer Science and Technology
Information distance and its extensions
DS'11 Proceedings of the 14th international conference on Discovery science
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If Kolmogorov complexity [25] measures information in one object and Information Distance measures information shared by two objects, how do we measure information shared by many objects? This paper provides an initial pragmatic study of this fundamental data mining question. Firstly, Em(x1,x2,...,xn) is defined to be the minimum amount of thermodynamic energy needed to convert from any xi to any xj. With this definition several theoretical problems have been solved. Second, our newly proposed theory is applied to select a comprehensive review and a specialized review from many reviews: (1) Core feature words, expanded words and dependent words are extracted respectively. (2) Comprehensive and specialized reviews are selected according to the information among them. This method of selecting a single review can be extended to select multiple reviews as well. Finally, experiments show that this comprehensive and specialized review mining method based on our new theory can do the job efficiently.