261,855 research outputs found
A Type-Safe Model of Adaptive Object Groups
Services are autonomous, self-describing, technology-neutral software units
that can be described, published, discovered, and composed into software
applications at runtime. Designing software services and composing services in
order to form applications or composite services requires abstractions beyond
those found in typical object-oriented programming languages. This paper
explores service-oriented abstractions such as service adaptation, discovery,
and querying in an object-oriented setting. We develop a formal model of
adaptive object-oriented groups which offer services to their environment.
These groups fit directly into the object-oriented paradigm in the sense that
they can be dynamically created, they have an identity, and they can receive
method calls. In contrast to objects, groups are not used for structuring code.
A group exports its services through interfaces and relies on objects to
implement these services. Objects may join or leave different groups. Groups
may dynamically export new interfaces, they support service discovery, and they
can be queried at runtime for the interfaces they support. We define an
operational semantics and a static type system for this model of adaptive
object groups, and show that well-typed programs do not cause
method-not-understood errors at runtime.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432
Association rules discovery from diagnostic data-application to gearboxes used in mining industry
One of the key issues encountered in development of condition monitoring systems for industry is definition of decision rules in diagnostic system for determined diagnostic features. In practice, it appears very often that proposed algorithm is not effective for all technical assets of machinery park. The major cause is usually related to smaller or higher diversity of objects, mainly in terms of design features, operating conditions and wear level. These factors directly influence the profile of measured vibration signals, diagnostic features, thresholds, decision rules and so on. In this paper authors propose the usage of Generalized Rule Induction (GRI) algorithm for association rules discovery from data base of the Computerized Maintenance Management System (CMMS) - patterns hidden in data reflecting existing processes phenomena, regularities, and expresses relationships between them. Such approach provides better interpretation of signals, and consequently, much more effective decision rules
A review of associative classification mining
Associative classification mining is a promising approach in data mining that utilizes the
association rule discovery techniques to construct classification systems, also known as
associative classifiers. In the last few years, a number of associative classification algorithms
have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms
employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule
evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative
classification techniques with regards to the above criteria. Finally, future directions in associative
classification, such as incremental learning and mining low-quality data sets, are also
highlighted in this paper
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 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 find substructures capable of compressing the original data
and to discover structural concepts important to the domain. Description of
Online Appendix: This is a compressed tar file containing the SUBDUE discovery
system, written in C. The program accepts as input databases represented in
graph form, and will output discovered substructures with their corresponding
value.Comment: See http://www.jair.org/ for an online appendix and other files
accompanying this articl
Image mining: trends and developments
[Abstract]: Advances in image acquisition and storage technology have led to tremendous growth in very large and detailed image databases. These images, if analyzed, can reveal useful information to the human users. Image mining deals with the extraction of implicit knowledge, image data relationship, or other patterns not explicitly stored in the images. Image mining is more than just an extension of data mining to image domain. It is an interdisciplinary endeavor that draws upon expertise in computer vision, image processing, image retrieval, data mining, machine learning, database, and artificial intelligence. In this paper, we will examine the research issues in image mining, current developments in image mining, particularly, image mining frameworks, state-of-the-art techniques and systems. We will also identify some future research directions for image mining
QCBA: Postoptimization of Quantitative Attributes in Classifiers based on Association Rules
The need to prediscretize numeric attributes before they can be used in
association rule learning is a source of inefficiencies in the resulting
classifier. This paper describes several new rule tuning steps aiming to
recover information lost in the discretization of numeric (quantitative)
attributes, and a new rule pruning strategy, which further reduces the size of
the classification models. We demonstrate the effectiveness of the proposed
methods on postoptimization of models generated by three state-of-the-art
association rule classification algorithms: Classification based on
Associations (Liu, 1998), Interpretable Decision Sets (Lakkaraju et al, 2016),
and Scalable Bayesian Rule Lists (Yang, 2017). Benchmarks on 22 datasets from
the UCI repository show that the postoptimized models are consistently smaller
-- typically by about 50% -- and have better classification performance on most
datasets
Monitoring land use changes using geo-information : possibilities, methods and adapted techniques
Monitoring land use with geographical databases is widely used in decision-making. This report presents the possibilities, methods and adapted techniques using geo-information in monitoring land use changes. The municipality of Soest was chosen as study area and three national land use databases, viz. Top10Vector, CBS land use statistics and LGN, were used. The restrictions of geo-information for monitoring land use changes are indicated. New methods and adapted techniques improve the monitoring result considerably. Providers of geo-information, however, should coordinate on update frequencies, semantic content and spatial resolution to allow better possibilities of monitoring land use by combining data sets
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Machine learning : techniques and foundations
The field of machine learning studies computational methods for acquiring new knowledge, new skills, and new ways to organize existing knowledge. In this paper we present some of the basic techniques and principles that underlie AI research on learning, including methods for learning from examples, learning in problem solving, learning by analogy, grammar acquisition, and machine discovery. In each case, we illustrate the techniques with paradigmatic examples
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