The novel rule induction approach to dynamic big data in green energy

With concerns about climate change growing it could be that green energy will begin to play a major role. Green Energy requires to resolve the optimization problem of electronic distribution, control, and storage with decision rule support. Due to the characteristics of green energy data nature - time dependency and variance, and big data, a novel approach to induct rules is required without re-computing rule sets from the very beginning, when new objects are updated to information system. The proposed approach updates rule sets by partly modifying original rule sets, hence a lot of time are saved, and it is especially useful when extracting rules from big data sets. The rules comparison helps decision maker to explore the marketing and qualified decision for renew energy distribution.

[1]  I. Dincer Environmental impacts of energy , 1999 .

[2]  Tong Lingyun,et al.  Incremental learning of decision rules based on rough set theory , 2002, Proceedings of the 4th World Congress on Intelligent Control and Automation (Cat. No.02EX527).

[3]  Andrew Kusiak,et al.  Feature transformation methods in data mining , 2001 .

[4]  Huan Liu,et al.  Handling concept drifts in incremental learning with support vector machines , 1999, KDD '99.

[5]  K. Riahi,et al.  The hydrogen economy in the 21st century: a sustainable development scenario , 2003 .

[6]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[7]  R. Hallowell The relationships of customer satisfaction, customer loyalty, and profitability: an empirical study , 1996 .

[8]  Rolf Wüstenhagen,et al.  Green Energy Market Development in Germany: Effective Public Policy and Emerging Customer Demand , 2006 .

[9]  Pan Yunhe,et al.  An incremental rule extracting algorithm based on Pawlak reduction , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[10]  Wen-Yau Liang,et al.  A rough set based approach to patent development with the consideration of resource allocation , 2011, Expert Syst. Appl..

[11]  Andrzej Skowron,et al.  Rudiments of rough sets , 2007, Inf. Sci..

[12]  K.,et al.  The Hydrogen Economy in the 21 st Century : A Sustainable Development Scenario , 2016 .

[13]  Nick Cercone,et al.  Integrating rough set theory and medical applications , 2008, Appl. Math. Lett..

[14]  Ajith Abraham,et al.  A new weighted rough set framework based classification for Egyptian NeoNatal Jaundice , 2012, Appl. Soft Comput..

[15]  Tzung-Pei Hong,et al.  An incremental mining algorithm for maintaining sequential patterns using pre-large sequences , 2011, Expert Syst. Appl..

[16]  Josef Kittler,et al.  Incremental Learning of Locally Orthogonal Subspaces for Set-based Object Recognition , 2006, BMVC.

[17]  Li Liu,et al.  Mining Dynamic databases by Weighting , 2003, Acta Cybern..

[18]  Ming-Hsuan Yang,et al.  Incremental Learning for Visual Tracking , 2004, NIPS.

[19]  I. Dincer Renewable energy and sustainable development: a crucial review , 2000 .

[20]  Soteris A. Kalogirou,et al.  Soft Computing in Green and Renewable Energy Systems , 2011, Studies in Fuzziness and Soft Computing.

[21]  Hans-Peter Kriegel,et al.  Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.

[22]  David Elliott,et al.  Prospects for renewable energy and green energy markets in the UK , 1999 .

[23]  Stefan Rüping,et al.  Incremental Learning with Support Vector Machines , 2001, ICDM.

[24]  Chowdhury Mofizur Rahman,et al.  Association rule mining in dynamic database using the concept of border sets , 2003 .

[25]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery in Databases , 1996, AI Mag..

[26]  Chun-Che Huang,et al.  Rough set-based approach to feature selection in customer relationship management , 2007 .

[27]  Masahiro Inuiguchi Several Approaches to Attribute Reduction in Variable Precision Rough Set Model , 2005, MDAI.

[28]  Srinivasan Parthasarathy,et al.  Mining frequent itemsets in distributed and dynamic databases , 2003, Third IEEE International Conference on Data Mining.

[29]  Clive Morley,et al.  A dynamic international demand model , 1998 .

[30]  Richard Weber,et al.  A methodology for dynamic data mining based on fuzzy clustering , 2005, Fuzzy Sets Syst..

[31]  Arkady B. Zaslavsky,et al.  Sensing as a Service and Big Data , 2013, ArXiv.

[32]  Shichao Zhang,et al.  A decremental algorithm of frequent itemset maintenance for mining updated databases , 2009, Expert Syst. Appl..

[33]  Chengqi Zhang,et al.  EDUA: An efficient algorithm for dynamic database mining , 2007, Inf. Sci..

[34]  Wojciech Ziarko,et al.  DATA‐BASED ACQUISITION AND INCREMENTAL MODIFICATION OF CLASSIFICATION RULES , 1995, Comput. Intell..

[35]  J. Bowen,et al.  The relationship between customer loyalty and customer satisfaction , 2001 .