12,714 research outputs found
Random Indexing K-tree
Random Indexing (RI) K-tree is the combination of two algorithms for
clustering. Many large scale problems exist in document clustering. RI K-tree
scales well with large inputs due to its low complexity. It also exhibits
features that are useful for managing a changing collection. Furthermore, it
solves previous issues with sparse document vectors when using K-tree. The
algorithms and data structures are defined, explained and motivated. Specific
modifications to K-tree are made for use with RI. Experiments have been
executed to measure quality. The results indicate that RI K-tree improves
document cluster quality over the original K-tree algorithm.Comment: 8 pages, ADCS 2009; Hyperref and cleveref LaTeX packages conflicted.
Removed clevere
Implicit solutions with consistent additive and multiplicative components
Use of multiple-point-constraint
Prospects and limitations of full-text index structures in genome analysis
The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared
Machine Learning Techniques for Stellar Light Curve Classification
We apply machine learning techniques in an attempt to predict and classify
stellar properties from noisy and sparse time series data. We preprocessed over
94 GB of Kepler light curves from MAST to classify according to ten distinct
physical properties using both representation learning and feature engineering
approaches. Studies using machine learning in the field have been primarily
done on simulated data, making our study one of the first to use real light
curve data for machine learning approaches. We tuned our data using previous
work with simulated data as a template and achieved mixed results between the
two approaches. Representation learning using a Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN) produced no successful predictions, but our work
with feature engineering was successful for both classification and regression.
In particular, we were able to achieve values for stellar density, stellar
radius, and effective temperature with low error (~ 2 - 4%) and good accuracy
(~ 75%) for classifying the number of transits for a given star. The results
show promise for improvement for both approaches upon using larger datasets
with a larger minority class. This work has the potential to provide a
foundation for future tools and techniques to aid in the analysis of
astrophysical data.Comment: Accepted to The Astronomical Journa
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