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 pre-selected and for which labeled training data is available. In contrast, we introduce an automated approach to activity tracking that identifies frequent activities that naturally occur in an individual's routine. With this capability we can then track the occurrence of regular activities to monitor functional health and to detect changes in an individual's 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.

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 identi es similar, but not identical, instances of a substructure and nds an approximate measure of closeness of two substructures when under computational constraints. In addition to the minimum description 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 Subdue's ability to nd substructures capable of compressing the original data and to discover structural concepts important to the domain.

at Arlington THE LARGE AMOUNT OF DATA collected today is quickly overwhelming researchers' abilities to interpret the data and discover interesting patterns in it. In response to this problem, researchers have developed techniques and systems for discovering concepts in databases. 1-3 Much of the collected data, however, has an explicit or implicit structural component (spatial or temporal), which few discovery systems are designed to handle. 4 So, in addition to the need to accelerate data mining of large databases, there is an urgent need to develop scalable tools for discovering concepts in structural databases. One method for discovering knowledge in structural data is the identification of common substructures within the data. Substructure discovery is the process of identifying concepts describing interesting and repetitive substructures within structural data. The discovered substructure concepts allow abstraction from the detailed data structure and provide relevant attributes for interpreting the data. The substructure discovery method is the basis of Subdue, which performs data mining on databases represented as graphs. The system performs two key data-mining techniques: unsupervised pattern discovery and supervised concept learning from examples. Our test applications have demonstrated the scalability and effectiveness of these techniques on a variety of structural databases.

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Hierarchical conceptual clustering has been proven to be a useful data mining technique. Graph-based representation of structural information has been shown to be successful in knowledge discovery. The Subdue substructure discovery system provides the advantages of both approaches. In this paper we present Subdue and focus on its clustering capabilities. We use two examples to illustrate the validity of the approach both in structured and unstructured domains, as well as compare Subdue to an earlier clustering algorithm.

Abstract

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.

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