21 Result(s)
within
Conference Paper
Claudia Plant
Data Mining and Knowledge Discovery
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Chapter and Conference Paper
Poisson Graphical Granger Causality by Minimum Message Length
Graphical Granger models are popular models for causal inference among time series. In this paper we focus on the Poisson graphical Granger model where the time series follow Poisson distribution. We use minim...
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Chapter and Conference Paper
Utilizing Structure-Rich Features to Improve Clustering
For successful clustering, an algorithm needs to find the boundaries between clusters. While this is comparatively easy if the clusters are compact and non-overlap** and thus the boundaries clearly defined, ...
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Chapter and Conference Paper
RandomLink – Avoiding Linkage-Effects by Employing Random Effects for Clustering
We present here a new parameter-free clustering algorithm that does not impose any assumptions on the data. Based solely on the premise that close data points are more likely to be in the same cluster, it can ...
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Chapter and Conference Paper
Granger Causality for Heterogeneous Processes
Discovery of temporal structures and finding causal interactions among time series have recently attracted attention of the data mining community. Among various causal notions graphical Granger causality is we...
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Chapter and Conference Paper
Clustering of Mixed-Type Data Considering Concept Hierarchies
Most clustering algorithms have been designed only for pure numerical or pure categorical data sets while nowadays many applications generate mixed data. It arises the question how to integrate various types o...
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Chapter and Conference Paper
KMN - Removing Noise from K-Means Clustering Results
K-Means is one of the most important data mining techniques for scientists who want to analyze their data. But K-Means has the disadvantage that it is unable to handle noise data points. This paper proposes a ...
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Chapter and Conference Paper
Parameter Free Mixed-Type Density-Based Clustering
Nowadays many applications generate mixed data objects consisting of numerical and categorical attributes. Simultaneously dealing with mixed objects is more challenging and various approaches convert one type ...
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Chapter and Conference Paper
Information-Theoretic Non-redundant Subspace Clustering
A comprehensive understanding of complex data requires multiple different views. Subspace clustering methods open up multiple interesting views since they support data objects to be assigned to different clust...
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Chapter and Conference Paper
Novel Indexing Strategy and Similarity Measures for Gaussian Mixture Models
Efficient similarity search for data with complex structures is a challenging task in many modern data mining applications, such as image retrieval, speaker recognition and stock market analysis. A common way ...
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Chapter and Conference Paper
Knowledge Discovery of Complex Data Using Gaussian Mixture Models
With the explosive growth of data quantity and variety, the representation and analysis of complex data becomes a more and more challenging task in many modern applications. As a general class of probabilistic...
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Chapter and Conference Paper
Attributed Graph Clustering with Unimodal Normalized Cut
Graph vertices are often associated with attributes. For example, in addition to their connection relations, people in friendship networks have personal attributes, such as interests, age, and residence. Such ...
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Chapter and Conference Paper
Indexing Multiple-Instance Objects
As an actively investigated topic in machine learning, Multiple-Instance Learning (MIL) has many proposed solutions, including supervised and unsupervised methods. We introduce an indexing technique supporting...
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Chapter and Conference Paper
Covariate-Related Structure Extraction from Paired Data
In the biological domain, it is more and more common to apply several high-throughput technologies to the same set of samples. We propose a Covariate-Related Structure Extraction approach (CRSE) that explores ...
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Chapter and Conference Paper
Mining Medical Data to Obtain Fuzzy Predicates
The collection of methods known as ‘data mining’ offers methodological and technical solutions to deal with the analysis of medical data and the construction of models. Medical data have a special status based...
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Chapter and Conference Paper
Centroid Clustering of Cellular Lineage Trees
Trees representing hierarchical knowledge are prevalent in biology and medicine. Some examples are phylogenetic trees, the hierarchical structure of biological tissues and cell lines. The increasing throughput...
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Chapter and Conference Paper
Segmentation and Kinetic Analysis of Breast Lesions in DCE-MR Imaging Using ICA
Dynamic Contrast Enhance-Magnetic Resonance Imaging (DCE-MRI) has proved to be a useful tool for diagnosing mass-like breast cancer. For non-mass-like lesions, however, no methods applied on DCE-MRI have shown...
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Chapter and Conference Paper
Robust Synchronization-Based Graph Clustering
Complex graph data now arises in various fields like social networks, protein-protein interaction networks, ecosystems, etc. To reveal the underlying patterns in graphs, an important task is to partition them ...
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Chapter and Conference Paper
Integrative Parameter-Free Clustering of Data with Mixed Type Attributes
Integrative mining of heterogeneous data is one of the major challenges for data mining in the next decade. We address the problem of integrative clustering of data with mixed type attributes. Most existing so...
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Chapter and Conference Paper
SkyDist: Data Mining on Skyline Objects
The skyline operator is a well established database primitive which is traditionally applied in a way that only a single skyline is computed. In this paper we use multiple skylines themselves as objects for da...
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Chapter and Conference Paper
ITCH: Information-Theoretic Cluster Hierarchies
Hierarchical clustering methods are widely used in various scientific domains such as molecular biology, medicine, economy, etc. Despite the maturity of the research field of hierarchical clustering, we have i...
