Towards a general theory of action and time
Artificial Intelligence
Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Event detection from time series data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Deriving Trends in Historical and Real-Time Continuously Sampled Medical Data
Journal of Intelligent Information Systems - Special issue on integrating artificial intelligene and database technologies
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
The Representation Race - Preprocessing for Handling Time Phenomena
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Learning Patterns of Behavior by Observing System Events
ECML '00 Proceedings of the 11th European Conference on Machine Learning
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
MSTS: A System for Mining Sets of Time Series
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Pattern Extraction for Time Series Classification
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Detecting Temporal Change in Event Sequences: An Application to Demographic Data
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
A Classification Approach for Prediction of Target Events in Temporal Sequences
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Using Dynamic Time Warping to Bootstrap HMM-Based Clustering of Time Series
Sequence Learning - Paradigms, Algorithms, and Applications
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Discovery of Core Episodes from Sequences
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Detecting Interesting Instances
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
An Unsupervised Algorithm for Segmenting Categorical Timeseries into Episodes
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Determining Hit Rate in Pattern Search
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Complex Data: Mining Using Patterns
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
| Hi-index | 0.00 |
Time-stamped data occur frequently in real-world databases. The goal of analysing time-stamped data is very often to find a small group of objects (customers, machine parts,...) which is important for the business at hand. In contrast, the majority of objects obey well-known rules and is not of interest for the analysis. In terms of a classification task, the small group means that there are very few positive examples and within them, there is some sort of a structure such that the small group differs significantly from the majority. We may consider such a learning task learning a local pattern. Depending on the goal of the data analysis, different aspects of time are relevant, e.g., the particular date, the duration of a certain state, or the number of different states. From the given data, we may generate features that allow us to express the aspect of interest. Here, we investigate the aspect of state change and its representation for learning local patterns in time-stamped data. Besides a simple Boolean representation indicating a change, we use frequency features from information retrieval. We transfer Joachim's theory for text classification to our task and investigate its fit to local pattern learning. The approach has been implemented within the MiningMart system and was successfully applied to real-world insurance data.