Time series: theory and methods
Time series: theory and methods
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Outliers and data mining: finding exceptions in data
Outliers and data mining: finding exceptions in data
Novelty detection: a review—part 1: statistical approaches
Signal Processing
ACM Computing Surveys (CSUR)
CURIO: a fast outlier and outlier cluster detection algorithm for large datasets
AIDM '07 Proceedings of the 2nd international workshop on Integrating artificial intelligence and data mining - Volume 84
Data Mining for Business Applications: Introduction
Proceedings of the 2010 conference on Data Mining for Business Applications
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Hospitals are adept at capturing large volumes of highly multi-dimensional data about their activities including clinical, demographic, administrative, financial and, increasingly, outcome data (such as adverse events). Managing and understanding this data is difficult as hospitals typically do not have the staff and/or the expertise to assemble, query, analyse and report on the potential knowledge contained within such data. The Power Knowledge Builder (PKB) project investigated the adaption of data mining algorithms to the domain of patient costing, with the aim of helping practitioners better understand their data and therefore facilitate best practice.