Paper Info
Title
Efficient rule based structural algorithms for classification of tree structured data
Abstract
Recently, tree structures have become a popular way for storing and manipulating huge amount of data. Classification of these data can facilitate storage, retrieval, indexing, query answering and different processing operations. In this paper, we present C-Classifier and M-Classifier algorithms for rule based classification of tree structured data. These algorithms are based on extracting especial tree patterns from training dataset. These tree patterns, i.e. closed tree patterns and maximal tree patterns are capable of extracting characteristics of training trees completely and non-redundantly. Our experiments show that M-Classifier significantly reduces running time and complexity. As experimental results show, accuracies of M-Classifier and C-Classifier depend on whether or not we want to classify all of the data points (even uncovered data). In the case of complete classification, C-Classifier shows the best classification quality. On the other hand and in the case of partial classification, M-Classifier improves classification quality measures.
Year
DOI
Venue
2009
10.3233/IDA-2009-0361
Intell. Data Anal.
Keywords
Field
DocType
complete classification,tree structure,structural algorithm,best classification quality,data point,especial tree pattern,classification quality measure,tree pattern,maximal tree pattern,efficient rule,training tree,partial classification,rule based
Data mining,Computer science,Classification Tree Method,K-ary tree,Artificial intelligence,Tree structure,Interval tree,Tree traversal,Pattern recognition,Algorithm,Segment tree,Machine learning,Incremental decision tree,Search tree
Journal
Volume
Issue
ISSN
13
1
1088-467X
Citations 
PageRank 
References 
3
0.37
29
Authors
5
Name
Order
Citations
PageRank
Mostafa Haghir Chehreghani1508.46
Morteza Haghir Chehreghani211016.07
Caro Lucas31501103.34
Masoud Rahgozar4728.77
Euhanna Ghadimi527513.75