Discovering Chinese words from unsegmented text (poster abstract)
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
On the approximation of curves by line segments using dynamic programming
Communications of the ACM
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Finding recurrent sources in sequences
RECOMB '03 Proceedings of the seventh annual international conference on Research in computational molecular biology
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
An Algorithm for Segmenting Categorical Time Series into Meaningful Episodes
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Unsupervised Clustering of Symbol Strings and Context Recognition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration
Data Mining and Knowledge Discovery
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
On lossy time decompositions of time stamped documents
Proceedings of the thirteenth ACM international conference on Information and knowledge management
ACM SIGMOD Record
Discovering important nodes through graph entropy the case of Enron email database
Proceedings of the 3rd international workshop on Link discovery
Information Preserving Time Decompositions of Time Stamped Documents*
Data Mining and Knowledge Discovery
Optimal Segmentation Using Tree Models
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Who Thinks Who Knows Who? Socio-cognitive Analysis of Email Networks
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Time-Series Segmentation Using Predictive Modular Neural Networks
Neural Computation
A segmentation-based approach for temporal analysis of software version repositories
Journal of Software Maintenance and Evolution: Research and Practice
Efficient algorithms for constructing time decompositions of time stamped documents
DEXA'05 Proceedings of the 16th international conference on Database and Expert Systems Applications
An approach for temporal analysis of email data based on segmentation
Data & Knowledge Engineering
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We propose a special type of time series, which we call an item-set time series, to facilitate the temporal analysis of software version histories, email logs, stock market data, etc. In an item-set time series, each observed data value is a set of discrete items. We formalize the concept of an item-set time series and present efficient algorithms for segmenting a given item-set time series. Segmentation of a time series partitions the time series into a sequence of segments where each segment is constructed by combining consecutive time points of the time series. Each segment is associated with an item set that is computed from the item sets of the time points in that segment, using a function which we call a measure function. We then define a concept called the segment difference, which measures the difference between the item set of a segment and the item sets of the time points in that segment. The segment difference values are required to construct an optimal segmentation of the time series. We describe novel and efficient algorithms to compute segment difference values for each of the measure functions described in the paper. We outline a dynamic programming based scheme to construct an optimal segmentation of the given item-set time series. We use the item-set time series segmentation techniques to analyze the temporal content of three different data sets---Enron email, stock market data, and a synthetic data set. The experimental results show that an optimal segmentation of item-set time series data captures much more temporal content than a segmentation constructed based on the number of time points in each segment, without examining the item set data at the time points, and can be used to analyze different types of temporal data.