1,542 research outputs found
Latent class analysis for segmenting preferences of investment bonds
Market segmentation is a key component of conjoint analysis which addresses consumer
preference heterogeneity. Members in a segment are assumed to be homogenous in their
views and preferences when worthing an item but distinctly heterogenous to members of other
segments. Latent class methodology is one of the several conjoint segmentation procedures
that overcome the limitations of aggregate analysis and a-priori segmentation. The main
benefit of Latent class models is that market segment membership and regression parameters
of each derived segment are estimated simultaneously. The Latent class model presented in
this paper uses mixtures of multivariate conditional normal distributions to analyze rating
data, where the likelihood is maximized using the EM algorithm. The application focuses on
customer preferences for investment bonds described by four attributes; currency, coupon
rate, redemption term and price. A number of demographic variables are used to generate
segments that are accessible and actionable.peer-reviewe
Survey of data mining approaches to user modeling for adaptive hypermedia
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
Learning Hybrid Neuro-Fuzzy Classifier Models From Data: To Combine or Not to Combine?
To combine or not to combine? Though not a question of the same gravity as the Shakespeare’s to be or not
to be, it is examined in this paper in the context of a hybrid neuro-fuzzy pattern classifier design process. A general fuzzy
min-max neural network with its basic learning procedure is used within six different algorithm independent learning
schemes. Various versions of cross-validation, resampling techniques and data editing approaches, leading to a generation
of a single classifier or a multiple classifier system, are scrutinised and compared. The classification performance on
unseen data, commonly used as a criterion for comparing different competing designs, is augmented by further four
criteria attempting to capture various additional characteristics of classifier generation schemes. These include: the ability
to estimate the true classification error rate, the classifier transparency, the computational complexity of the learning
scheme and the potential for adaptation to changing environments and new classes of data. One of the main questions
examined is whether and when to use a single classifier or a combination of a number of component classifiers within a
multiple classifier system
A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications
This survey samples from the ever-growing family of adaptive resonance theory
(ART) neural network models used to perform the three primary machine learning
modalities, namely, unsupervised, supervised and reinforcement learning. It
comprises a representative list from classic to modern ART models, thereby
painting a general picture of the architectures developed by researchers over
the past 30 years. The learning dynamics of these ART models are briefly
described, and their distinctive characteristics such as code representation,
long-term memory and corresponding geometric interpretation are discussed.
Useful engineering properties of ART (speed, configurability, explainability,
parallelization and hardware implementation) are examined along with current
challenges. Finally, a compilation of online software libraries is provided. It
is expected that this overview will be helpful to new and seasoned ART
researchers
Clustering Algorithms: Their Application to Gene Expression Data
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure
A single currency for Asia? Evaluation and comparison using hierarchical and model-based cluster analysis
Today, there is increased speculation on the possibility of an Asian currency, as the region begins to show increased promise as a region of nascent economic activity. Any
monetary integration scheme in East Asia would likely have to include both China and India though, so this paper attempts to assess the evolution of convergence among the
East Asian countries, including China and India, according to the optimum currency area theory criteria, which is operationalized through the use of cluster analysis.
In this paper we use both traditional "hierarchical" clustering as well as the more recently developed "model-based" clustering techniques and compare the outcome in
each case. As the East Asian crisis of 1997-98 is likely to a¤ect the results, the exercise is done for pre-crisis, crisis, and post-crisis periods. The results reveal some
structure among the countries, an increase in the degree of subregional homogeneity, and a robust relationship between Malaysia and Singapore
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
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