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.

Finding frequent patterns plays a key role in exploring ASSOCIATION patterns, correlation, and many other interesting relationships that are applicable in TDB. Several ASSOCIATION RULE MINING algorithms such as Apriori, FP-Growth, and Eclat have been proposed in the literature. FP-Growth algorithm construct a tree structure from transaction database and recursively traverse this tree to extract frequent patterns which satisfies the minimum support in a depth first search manner. Because of its high efficiency, several frequent pattern MINING methods and algorithms have used FP-Growth’s depth first exploration idea to mine frequent patterns. These algorithms change the FP-tree structure to improve efficiency. In this paper, we propose a new frequent pattern MINING algorithm based on FP-Growth idea which is using a bit matrix and a linked list structure to extract frequent patterns. The bit matrix transforms the dataset and prepares it to construct as a linked list which is used by our new FPBitLink Algorithm. Our performance study and experimental results show that this algorithm outperformed the former algorithms.

Data sanitization process is used to promote the sharing of transactional databases among organizations and businesses, and alleviates concerns for individuals and organizations regarding the disclosure of sensitive patterns. It transforms the source database into a released database so that counterparts cannot discover the sensitive patterns and so data confidentiality is preserved against ASSOCIATION RULE MINING method. This process strongly relies on the minimizing the impact of data sanitization on the data utility by minimizing the number of lost patterns in the form of non-sensitive patterns which are not mined from sanitized database. This study proposes a data sanitization algorithm to hide sensitive patterns in the form of frequent item sets from the database while controlling the impact of sanitization on the data utility using estimation of impact factor of each modification on non-sensitive item sets. The proposed algorithm has been compared with Sliding Window size Algorithm (SWA) and Max-Min1 in terms of execution time, data utility and data accuracy. The data accuracy is defined as the ratio of deleted items to the total support values of sensitive item sets in the source dataset. Experimental results demonstrate that the proposed algorithm outperforms SWA and Max-Min1 in terms of maximizing the data utility and data accuracy and it provides better execution time over SWA and Max-Min1 in high scalability for sensitive item sets and transactions.

Objective: There are many climatic factors affecting the number of patients in hospitals which generally tend to make a Non-optimal use of their facilities and human resources. This research is aimed at discovering hidden knowledge between climatic factors and the number of hospital patients using data MINING techniques. Methods: In this study, the relationship between climatic factors and the number of patients in Dr. Sheikh specialized pediatric hospital of Mashhad is investigated by classification based on multidimensional ASSOCIATION RULE MINING. The number of patients in the nephrology, hematology, emergency and PICU department of this hospital have been considered separately, and consequently the relationship between the number of patients and the climatic factors such as air temperature, relative humidity, wind speed, air pressure and air pollution have been analyzed. This research has analyzed data gathered through a 19 month period and has been obtained by referring to the documents. In this research for feature selection, all subsets of climatic factors are searched and the effect of all subsets on the number of patients are evaluated using linear regression. Also for RULE MINING is used classification based on multidimensional ASSOCIATION RULE MINING which is based on known Apriori algorithm. Results: The results show different patterns that indicate the relationship between the number of patients in the hospital departments with the climatic factors. Conclusion: This study is able to help analyze the relationship between the climatic factors and the number of patients in the hospital. Also, the RULEs will help managers make optimal planning for hospital resources according to the different number of patients.