Lawrence B. Holder

Publications
Influence

Substructure Discovery Using Minimum Description Length and Background Knowledge

D. Cook, Lawrence B. Holder
Mathematics, Computer Science
J. Artif. Intell. Res.
1 August 1993

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. Expand

Graph-Based Data Mining

D. Cook, Lawrence B. Holder
Computer Science
IEEE Intell. Syst.
1 March 2000

Using databases represented as graphs, the Subdue system performs two key data mining techniques: unsupervised pattern discovery and supervised concept learning from examples. Expand

Substucture Discovery in the SUBDUE System

Lawrence B. Holder, D. Cook, Surnjani Djoko
Mathematics, Computer Science
KDD Workshop
31 July 1994

This paper describes the SUBDUE system, which uses the minimum description length principle to discover substructures that compress the database and represent structural concepts in the data. Expand

Discovering Activities to Recognize and Track in a Smart Environment

Parisa Rashidi, D. Cook, Lawrence B. Holder, M. Schmitter-Edgecombe
Computer Science, Medicine
IEEE Transactions on Knowledge and Data…
1 April 2011

The machine learning and pervasive sensing technologies found in smart homes offer unprecedented opportunities for providing health monitoring and assistance to individuals experiencing difficulties living independently at home. Expand

Mining Graph Data

D. Cook, Lawrence B. Holder
Computer Science
1 September 2006

Preface. Acknowledgments. Contributors. 1 INTRODUCTION (Lawrence B. Holder and Diane J. Cook). 1.1 Terminology. 1.2 Graph Databases. 1.3 Book Overview. References. Part I GRAPHS. 2 GRAPH… Expand

Application of Graph-Based Concept Learning to the Predictive Toxicology Domain

J. A. González, Lawrence B. Holder, D. Cook
Computer Science
2001

We present a graph-based concept learning system using graphs to represent the examples of the concept to learn. Expand

Graph-based hierarchical conceptual clustering

Istvan Jonyer, D. Cook, Lawrence B. Holder
Mathematics
1 March 2002

Hierarchical conceptual clustering has proven to be a useful, although under-explored, data mining technique. A graph-based representation of structural information combined with a substructure… Expand

Conditional random fields for activity recognition in smart environments

E. Nazerfard, Barnan Das, Lawrence B. Holder, D. Cook
Computer Science
IHI
11 November 2010

We describe the use of another probabilistic model: Conditional Random Fields (CRFs), which is currently gaining popularity for its remarkable performance for activity recognition. Expand

A Selectivity based approach to Continuous Pattern Detection in Streaming Graphs

S. Choudhury, Lawrence B. Holder, George Chin, Khushbu Agarwal, J. Feo
Computer Science
EDBT
2 February 2015

We introduce a "Lazy Search" algorithm where the search strategy is decided on a vertex-to-vertex basis depending on the likelihood of a match in the vertex neighborhood. Expand

Subdue: compression-based frequent pattern discovery in graph data

N. Ketkar, Lawrence B. Holder, D. Cook
Computer Science
21 August 2005

We describe a graph-based data mining system which focuses on the discovery of sub-graphs which are not only frequent but also compress the graph dataset, using a heuristic algorithm. Expand

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