A Structural Data Mining Approach for the Classification of Secondary RNA structure

Author(s):  
W.W.M. Lam ◽  
K.C.C. Chan
Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.


2017 ◽  
Vol 50 (14) ◽  
pp. 2292-2307 ◽  
Author(s):  
Camila Maione ◽  
Christian Turra ◽  
Elisabete A. De Nadai Fernandes ◽  
Márcio Arruda Bacchi ◽  
Fernando Barbosa ◽  
...  

2008 ◽  
Vol 34 (3) ◽  
pp. 607-623 ◽  
Author(s):  
Neri Kafkafi ◽  
Daniel Yekutieli ◽  
Greg I Elmer

2003 ◽  
Vol 02 (03) ◽  
pp. 445-457 ◽  
Author(s):  
Chien-Hsiung Lin ◽  
Yi-Hsin Liu

A set of data represented by a set of real numbers can be handled by the computer much easier than non-real valued data. This paper develops bicriteria linear program solution through a fuzzy mathematical programming approach which assigns a real number to each member of the data. This method integrates data information and the decision maker's objective opinion to construct a tool (function) of selection and classification.


Author(s):  
Agnieszka Ławrynowicz ◽  
Jędrzej Potoniec

The authors propose a new method for mining sets of patterns for classification, where patterns are represented as SPARQL queries over RDFS. The method contributes to so-called semantic data mining, a data mining approach where domain ontologies are used as background knowledge, and where the new challenge is to mine knowledge encoded in domain ontologies, rather than only purely empirical data. The authors have developed a tool that implements this approach. Using this the authors have conducted an experimental evaluation including comparison of our method to state-of-the-art approaches to classification of semantic data and an experimental study within emerging subfield of meta-learning called semantic meta-mining. The most important research contributions of the paper to the state-of-art are as follows. For pattern mining research or relational learning in general, the paper contributes a new algorithm for discovery of new type of patterns. For Semantic Web research, it theoretically and empirically illustrates how semantic, structured data can be used in traditional machine learning methods through a pattern-based approach for constructing semantic features.


Author(s):  
Meenu Gupta ◽  
Vijender Kumar Solanki ◽  
Vijay Kumar Singh ◽  
Vicente García-Díaz

Data mining is used in various domains of research to identify a new cause for tan effect in the society over the globe. This article includes the same reason for using the data mining to identify the Accident Occurrences in different regions and to identify the most valid reason for happening accidents over the globe. Data Mining and Advanced Machine Learning algorithms are used in this research approach and this article discusses about hyperline, classifications, pre-processing of the data, training the machine with the sample datasets which are collected from different regions in which we have structural and semi-structural data. We will dive into deep of machine learning and data mining classification algorithms to find or predict something novel about the accident occurrences over the globe. We majorly concentrate on two classification algorithms to minify the research and task and they are very basic and important classification algorithms. SVM (Support vector machine), CNB Classifier. This discussion will be quite interesting with WEKA tool for CNB classifier, Bag of Words Identification, Word Count and Frequency Calculation.


2018 ◽  
Vol 8 (5) ◽  
pp. 165-168
Author(s):  
V.Y. Begeneev ◽  
◽  
I.A. Shmidt ◽  
B. Krause ◽  
◽  
...  

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