Representation of Concept Hierarchy using an Efficient Encoding Scheme

The premise of this paper is to use an efficient encoding scheme which will be used to encode high level concept hierarchy of a transactional table. This table will work as the base to generate multiple level association rules. These rules discovers the hidden knowledge align at higher level of abstraction. Therefore the numeric encoding of the concept hierarchy improves the time complexity and space complexity of task relevant data.

[1]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[2]  Michelangelo Ceci,et al.  Mining and Filtering Multi-level Spatial Association Rules with ARES , 2005, ISMIS.

[3]  Donald D. Chamberlin,et al.  Using the New DB2: IBM's Object-Relational Database System , 1996 .

[4]  Mario Cannataro,et al.  Mining Association Rules from Gene Ontology and Protein Networks: Promises and Challenges , 2014, ICCS.

[5]  Igor Kononenko,et al.  Multi-level association rules and directed graphs for spatial data analysis , 2013, Expert Syst. Appl..

[6]  Kawuu W. Lin,et al.  A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments , 2010, Int. J. Ad Hoc Ubiquitous Comput..

[7]  Ryszard S. Michalski,et al.  Inductive Learning as Rule-Guided Generalization and Conceptual Simplification of Symbolic Descriptions: Unifying principles and a methodology , 1980 .

[8]  Hu Zhengbing,et al.  A Novel Network Intrusion Detection System (NIDS) Based on Signatures Search of Data Mining , 2008, First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008).

[9]  R. Wille Concept lattices and conceptual knowledge systems , 1992 .

[10]  Li-Yong Ren,et al.  Using data mining to discover signatures in network-based intrusion detection , 2002, Proceedings. International Conference on Machine Learning and Cybernetics.

[11]  Jiawei Han,et al.  Mining knowledge at multiple concept levels , 1995, CIKM '95.

[12]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[13]  Wen Wang,et al.  Research on Multi-Level Association Rules Based on Geosciences Data , 2013, J. Softw..