Attribute-oriented induction in data mining
Advances in knowledge discovery and data mining
ACM Computing Surveys (CSUR)
SPARTAN: a model-based semantic compression system for massive data tables
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Semantic Compression and Pattern Extraction with Fascicles
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Knowledge Discovery in Databases: An Attribute-Oriented Approach
VLDB '92 Proceedings of the 18th International Conference on Very Large Data Bases
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
ItCompress: An Iterative Semantic Compression Algorithm
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Communications of the ACM - Designing for the mobile device
General purpose database summarization
VLDB '05 Proceedings of the 31st international conference on Very large data bases
ACM SIGMOD Record
Summarization — Compressing Data into an Informative Representation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On efficiently summarizing categorical databases
Knowledge and Information Systems
Constructing comprehensive summaries of large event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Succinct summarization of transactional databases: an overlapped hyperrectangle scheme
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving a large scale taxonomy from Wikipedia
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Clustering of time series data-a survey
Pattern Recognition
Automatic taxonomy generation: issues and possibilities
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Data stream synopsis using saintetiq
FQAS'06 Proceedings of the 7th international conference on Flexible Query Answering Systems
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Data summarization model for user action log files
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
MOETA: a novel text-mining model for collecting and analysing competitive intelligence
International Journal of Advanced Media and Communication
| Hi-index | 0.00 |
In this paper, we present the concept of Time Sequence Summarization to support chronology-dependent applications on massive data sources. Time sequence summarization takes as input a time sequence of events that are chronologically ordered. Each event is described by a set of descriptors. Time sequence summarization produces a concise time sequence that can be substituted for the original time sequence in chronology-dependent applications. We propose an algorithm that achieves time sequence summarization based on a generalization, grouping and concept formation process. Generalization expresses event descriptors at higher levels of abstraction using taxonomies while grouping gathers similar events. Concept formation is responsible for reducing the size of the input time sequence of events by representing each group created by one concept. The process is performed in a way such that the overall chronology of events is preserved. The algorithm computes the summary incrementally and has reduced algorithmic complexity. The resulting output is a concise representation, yet, informative enough to directly support chronology-dependent applications. We validate our approach by summarizing one year of financial news provided by Reuters.