List of Articles Association Rule

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  1 - Privacy Preserving Big Data Mining: Association Rule Hiding
  Golnar Assadat  Afzali shahriyar mohammadi
  10.7508/jist.2016.02.001
  Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to More

  Data repositories contain sensitive information which must be protected from unauthorized access. Existing data mining techniques can be considered as a privacy threat to sensitive data. Association rule mining is one of the utmost data mining techniques which tries to cover relationships between seemingly unrelated data in a data base.. Association rule hiding is a research area in privacy preserving data mining (PPDM) which addresses a solution for hiding sensitive rules within the data problem. Many researches have be done in this area, but most of them focus on reducing undesired side effect of deleting sensitive association rules in static databases. However, in the age of big data, we confront with dynamic data bases with new data entrance at any time. So, most of existing techniques would not be practical and must be updated in order to be appropriate for these huge volume data bases. In this paper, data anonymization technique is used for association rule hiding, while parallelization and scalability features are also embedded in the proposed model, in order to speed up big data mining process. In this way, instead of removing some instances of an existing important association rule, generalization is used to anonymize items in appropriate level. So, if necessary, we can update important association rules based on the new data entrances. We have conducted some experiments using three datasets in order to evaluate performance of the proposed model in comparison with Max-Min2 and HSCRIL. Experimental results show that the information loss of the proposed model is less than existing researches in this area and this model can be executed in a parallel manner for less execution time Manuscript profile
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  2 - Improvement of mining association rules in selecting knowledge groups in the project team with using data mining techniques combined by TOPSIS technique
  skch skch Ahmad نورنگ majid hasani دانيال  بيدگلي
  The role of knowledge management in project management is important and appropriate implementation of this is fairly effective in the efficiency of project management. Identification and using of knowledge groups is one of the principal components of knowledge manageme More

  The role of knowledge management in project management is important and appropriate implementation of this is fairly effective in the efficiency of project management. Identification and using of knowledge groups is one of the principal components of knowledge management. Using association rules to identify the relationship between knowledge groups of the project team can improve and increase utilization of knowledge groups. In order to discover association rules between variables, there are different methodologies that can lead to different results. In this study considered utilization of three methodology to discovery initial of association rules in knowledge groups between project team members.The identified basic rules are checked with multi-criteria decision making technique and then classified. The results of this study as for utilizing a combination of methodologies of discovering the association rules and final classificating rules with TOPSIS technique can introduce the association rules among knowledge groups in project teams that these results lead to the promotion of knowledge management performance in project management. Manuscript profile