Chandrasekar Ravi ¹, Neelu Khare ¹, N. Deepa ¹

Affiliations
1 School of Information Technology and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

Association Rule Mining (ARM) is a well recognized and interesting area of research in the field of Data Mining. In ARM, significant amount of research work has been reported. On the contrary, very less work has been reported for Negative Association Rule Mining (NARM). ARM concentrates only on positive rules while NARM explores negative rules. Also, most researchers have worked with discrete transaction dataset. So, in this paper, we propose a technique called Negative and Positive Fuzzy Association Rule Mining (NP-FARM), which mines both negative and positive association rules from a fuzzy transaction dataset. NP-FARM algorithm has been implemented and the experimental results determine the optimal minimum support threshold and optimal minimum confidence threshold for the given dataset. Also, the experimental results demonstrate that, as the size of the dataset increases, a negligible change in execution time is witnessed to mine the growing dataset.

Keywords

Association Rule Mining. Fuzzy Transactions, Positive And Negative Item Sets

Full Text

   

U. Kumaran ¹, Neelu Khare ¹

Affiliations
1 School of Information Technology and Engineering, VIT University, Vellore - 632014, Tamil Nadu, IN

Abstract

With the widespread habituate of data mining technology in the entire available sectors (public and private) elevate concerns about the sensitiveness of data being mined. Data mining is an enormously powerful technology to extract information from raw data. With the growth of ease of handiness of digital data the possibility of misapply of the data and the mined information grows. A key challenge is to build up security and privacy methods suitable for data mining. This is the reason PPDM (Privacy Preserving Data Mining) has acquired a steam in recent times. In this paper we have addressed the issue of PPDM and moreover, we have considered a scenario where two different parties possesses confidential databases of their own and wish to run a data mining algorithm on the union of their databases, without disclosing any unnecessary information, where we have suggested different methodologies in order to preserve the privacy in the data mining process one among them is Secure Multiparty Computation which is a field of cryptography. PPDM looks at the job of applying data mining algorithms on secret (confidential) data i.e. not granted to be disclosed even to the trusted party who’s running the algorithm.

Keywords

Cryptography, Data Mining Algorithm, Data Mining, Digital Data, PPDM (Privacy Preserving Data Mining), Secure Multiparty Computation.

Full Text