New Frontiers in Applied Data Mining
PAKDD 2009 International Workshops, Bangkok, Thailand, April 27-30, 2009. Revised Selected Papers
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17 Result(s)
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English
Computer Science
Artificial Intelligence
Information Storage and Retrieval
Graham Williams
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Chapter and Conference Paper
The Integrated Delivery of Large-Scale Data Mining: The ACSys Data Mining Project
Data Mining draws on many technologies to deliver novel and actionable discoveries from very large collections of data. The Australian Government’s Cooperative Research Centre for Advanced Computational System...
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Chapter and Conference Paper
Estimating Episodes of Care Using Linked Medical Claims Data
Australia has extensive administrative health data collected by Commonwealth and state agencies. Using a unique cleaned and linked administrative health dataset we address the problem of empirically defining e...
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Chapter and Conference Paper
Association Rule Discovery with Unbalanced Class Distributions
There are many methods for finding association rules in very large data. However it is well known that most general association rule discovery methods find too many rules, many of which are uninteresting rules...
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Chapter and Conference Paper
Conceptual Mining of Large Administrative Health Data
Health databases are characterised by large number of records, large number of attributes and mild density. This encourages data miners to use methodologies that are more sensitive to health undustry specifics...
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Chapter and Conference Paper
Exploring Possible Adverse Drug Reactions by Clustering Event Sequences
Historically the identification of adverse drug reactions relies on manual processes whereby doctors and hospitals report incidences to a central agency. In this paper we suggest a data mining approach using a...
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Chapter and Conference Paper
Temporal Sequence Associations for Rare Events
In many real world applications, systematic analysis of rare events, such as credit card frauds and adverse drug reactions, is very important. Their low occurrence rate in large databases often makes it diffic...
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Article
On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms
Outlier detection is a fundamental issue in data mining, specifically in fraud detection, network intrusion detection, network monitoring, etc. SmartSifter is an outlier detection engine addressing this proble...
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Chapter and Conference Paper
Representing Association Classification Rules Mined from Health Data
An association classification algorithm has been developed to explore adverse drug reactions in a large medical transaction dataset with unbalanced classes. Rules discovered can be used to alert medical practi...
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Chapter and Conference Paper
Neighborhood Density Method for Selecting Initial Cluster Centers in K-Means Clustering
This paper presents a new method for effectively selecting initial cluster centers in k-means clustering. This method identifies the high density neighborhoods from the data first and then selects the central poi...
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Chapter
Identifying Risk Groups Associated with Colorectal Cancer
In this paper, we explore data mining techniques for the task of identifying and describing risk groups for colorectal cancer (CRC) from population based administrative health data. Association rule discovery,...
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Chapter and Conference Paper
A Survey of Open Source Data Mining Systems
Open source data mining software represents a new trend in data mining research, education and industrial applications, especially in small and medium enterprises (SMEs). With open source software an enterpris...
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Book and Conference Proceedings
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Chapter and Conference Paper
Hybrid Random Forests: Advantages of Mixed Trees in Classifying Text Data
Random forests are a popular classification method based on an ensemble of a single type of decision tree. In the literature, there are many different types of decision tree algorithms, including C4.5, CART an...
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Chapter and Conference Paper
Ensemble Clustering of High Dimensional Data with FastMap Projection
In this paper, we propose an ensemble clustering method for high dimensional data which uses FastMap projection to generate subspace component data sets. In comparison with popular random sampling and random p...
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Chapter and Conference Paper
Extensions to Quantile Regression Forests for Very High-Dimensional Data
This paper describes new extensions to the state-of-the-art regression random forests Quantile Regression Forests (QRF) for applications to high-dimensional data with thousands of features. We propose a new subsp...
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Chapter and Conference Paper
Stratified Over-Sampling Bagging Method for Random Forests on Imbalanced Data
Imbalanced data presents a big challenge to random forests (RF). Over-sampling is a commonly used sampling method for imbalanced data, which increases the number of instances of minority class to balance the c...
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Chapter and Conference Paper
TRIC: A Triples Corrupter for Knowledge Graphs
We study the problem of corrupting triples in Knowledge Graphs (KG) for the purpose of assisting anomaly detection and error detection techniques developed for KG quality enhancement. Our goal is to provide us...
