Lawrence B. Holder

Substructure discovery using minimum description length and background knowledge

The ability to identify interesting and repetitive substructures is an essential component to discovering knowledge in structural data. We describe a new version of our SUBDUE substructure discovery system based on the minimum description length principle. The SUBDUE system discovers substructures that compress the original data and represent structural concepts in the data. By replacing previously-discovered substructures in the data, multiple passes of SUBDUE produce a hierarchical description of the structural regularities in the data. SUBDUE uses a computationally-bounded inexact graph match that identifies similar, but not identical, instances of a substructure and finds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimumdescription length principle, other background knowledge can be used by SUBDUE to guide the search towards more appropriate substructures. Experiments in a variety of domains demonstrate SUBDUEs ability to find substructures capable of compressing the original data and to discover structural concepts important to the domain.

Graph-based data mining

Using databases represented as graphs, the Subdue system performs two key data mining techniques: unsupervised pattern discovery and supervised concept learning from examples. Applications to large structural databases demonstrate Subdues scalability and effectiveness.

Discovering Activities to Recognize and Track in a Smart Environment

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. In order to monitor the functional health of smart home residents, we need to design technologies that recognize and track activities that people normally perform as part of their daily routines. Although approaches do exist for recognizing activities, the approaches are applied to activities that have been preselected and for which labeled training data are available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individuals routine. With this capability, we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individuals patterns and lifestyle. In this paper, we describe our activity mining and tracking approach, and validate our algorithms on data collected in physical smart environments.

Managing Adaptive Versatile Environments

The goal of the MavHome project is to develop technologies to manage adaptive versatile environments. In this paper, we present a complete agent architecture for a single inhabitant intelligent environment and discuss the development, deployment, and techniques utilized in our working intelligent environments. Empirical evaluation of our approach has proven its effectiveness at reducing inhabitant interactions by 72.2%

Graph-based hierarchical conceptual clustering

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 discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides one such combination of approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains, as well as to compare SUBDUE to the Cobweb clustering algorithm. We also develop a new metric for comparing structurally-defined clusterings. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data.

Discovering Structural Anomalies in Graph-Based Data

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, particularly for fraud, but less work has been done in terms of detecting anomalies in graph-based data. While there has been some work that has used statistical metrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains. In this paper we present graph- based approaches to uncovering anomalies in domains where the anomalies consist of unexpected entity/relationship deviations that resemble non- anomalous behavior. Using synthetic and real-world data, we evaluate the effectiveness of these algorithms at discovering anomalies in a graph-based representation of data.

Decision-theoretic cooperative sensor planning

This paper describes a decision-theoretic approach to cooperative sensor planning between multiple autonomous vehicles executing a military mission. For this autonomous vehicle application, intelligent cooperative reasoning must be used to select optimal vehicle viewing locations and select optimal camera pan and tilt angles throughout the mission. Decisions are made in such a way as to maximize the value of information gained by the sensors while maintaining vehicle stealth. Because the mission involves multiple vehicles, cooperation can be used to balance the work load and to increase information gain. This paper presents the theoretical foundations of our cooperative sensor planning research and describes the application of these techniques to ARPAs Unmanned Ground Vehicle program.

Subdue: compression-based frequent pattern discovery in graph data

A majority of the existing algorithms which mine graph datasets target complete, frequent sub-graph discovery. We describe the graph-based data mining system Subdue which focuses on the discovery of sub-graphs which are not only frequent but also compress the graph dataset, using a heuristic algorithm. The rationale behind the use of a compression-based methodology for frequent pattern discovery is to produce a fewer number of highly interesting patterns than to generate a large number of patterns from which interesting patterns need to be identified. We perform an experimental comparison of Subdue with the graph mining systems gSpan and FSG on the Chemical Toxicity and the Chemical Compounds datasets that are provided with gSpan. We present results on the performance on the Subdue system on the Mutagenesis and the KDD 2003 Citation Graph dataset. An analysis of the results indicates that Subdue can efficiently discover best-compressing frequent patterns which are fewer in number but can be of higher interest.

Insider Threat Detection Using a Graph-Based Approach

The authors present the use of graph-based approaches to discovering anomalous instances of structural patterns in data that represent insider threat activity. The approaches presented search for activities that appear to match normal transactions, but in fact are structurally different. The authors show the usefulness of applying graph theoretic approaches to discovering suspicious insider activity in domains such as social network communications, business processes, and cybercrime. The authors present some performance results to show the effectiveness of our approaches, and then conclude with some ongoing research that combines numerical analysis with structure analysis, analyzes multiple normative patterns, and extends to dynamic graphs.

MDL-BASED CONTEXT-FREE GRAPH GRAMMAR INDUCTION AND APPLICATIONS

We present an algorithm for the inference of context-free graph grammars from examples. The algorithm builds on an earlier system for frequent substructure discovery, and is biased toward grammars that minimize description length. Grammar features include recursion, variables and relationships. We present an illustrative example, demonstrate the algorithms ability to learn in the presence of noise, and show real-world examples.