146,527 research outputs found
Space-efficient detection of unusual words
Detecting all the strings that occur in a text more frequently or less
frequently than expected according to an IID or a Markov model is a basic
problem in string mining, yet current algorithms are based on data structures
that are either space-inefficient or incur large slowdowns, and current
implementations cannot scale to genomes or metagenomes in practice. In this
paper we engineer an algorithm based on the suffix tree of a string to use just
a small data structure built on the Burrows-Wheeler transform, and a stack of
bits, where is the length of the string and
is the size of the alphabet. The size of the stack is except for very
large values of . We further improve the algorithm by removing its time
dependency on , by reporting only a subset of the maximal repeats and
of the minimal rare words of the string, and by detecting and scoring candidate
under-represented strings that in the string. Our
algorithms are practical and work directly on the BWT, thus they can be
immediately applied to a number of existing datasets that are available in this
form, returning this string mining problem to a manageable scale.Comment: arXiv admin note: text overlap with arXiv:1502.0637
Improved Periodicity Mining in Time Series Databases
Time series data represents information about real world phenomena and periodicity mining explores the interesting periodic behavior that is inherent in the data. Periodicity mining has numerous applications such as in weather forecasting, stock market prediction and analysis, pattern recognition, etc. Recently, the suffix tree, a powerful data structure that efficiently solves many strings related problems has been used to gather information about repeated substrings in the text and then perform periodicity mining. However, periodicity mining deals with large amounts of data which makes it difficult to perform mining in main memory due to the space constraints of the suffix tree. Thus, we first propose the use of the Compressed Suffix Tree (CST) for space efficient periodicity mining in very large datasets. Given the time-space trade-off that comes with any practical usage of the CST, we provide a comprehensive empirical analysis on the practical usage of CSTs and traditional suffix trees for periodicity mining.;Noise is an inherent part of practical time series data, and it is important to mine periods in spite of the noise. This leads to the problem of approximate periodicity mining. Existing algorithms have dealt with the noise introduced between the occurrences of the periodic pattern, but not the noise introduced in the structure of the pattern itself. We present a taxonomy for approximate periodicity and then propose an algorithm that performs periodicity mining in the presence of noise introduced simultaneously in both the structure of the pattern and between the periodic occurrences of the pattern
Development and testing of a text-mining approach to analyse patientsā comments on their experiences of colorectal cancer care
Background: Quality of cancer care may greatly impact upon patientsā health-related quality of life (HRQoL). Free-text responses to patient-reported outcome measures (PROMs) provide rich data but analysis is time and resource-intensive. This study developed and tested a learning-based text-mining approach to facilitate analysis of patientsā experiences of care and develop an explanatory model illustrating impact upon HRQoL.
Methods: Respondents to a population-based survey of colorectal cancer survivors provided free-text comments regarding their experience of living with and beyond cancer. An existing coding framework was tested and adapted, which informed learning-based text mining of the data. Machine-learning algorithms were trained to identify comments relating to patientsā specific experiences of service quality, which were verified by manual qualitative analysis. Comparisons between coded retrieved comments and a HRQoL measure (EQ5D) were explored.
Results: The survey response rate was 63.3% (21,802/34,467), of which 25.8% (n=5634) participants provided free-text comments. Of retrieved comments on experiences of care (n=1688), over half (n=1045, 62%) described positive care experiences. Most negative experiences concerned a lack of post-treatment care (n=191, 11% of retrieved comments), and insufficient information concerning self-management strategies (n=135, 8%) or treatment side effects (n=160, 9%). Associations existed between HRQoL scores and coded algorithm-retrieved comments. Analysis indicated that the mechanism by which service quality impacted upon HRQoL was the extent to which services prevented or alleviated challenges associated with disease and treatment burdens.
Conclusions: Learning-based text mining techniques were found useful and practical tools to identify specific free-text comments within a large dataset, facilitating resource-efficient qualitative analysis. This method should be considered for future PROM analysis to inform policy and practice. Study findings indicated that perceived care quality directly impacts upon HRQoL
- ā¦