Balanced Constraint Measure Algorithm to Preserve Privacy from Sequential Rule Discovery

Preservation of needed privacy from mining algorithms (data mining methods which extract information from the privacy diffusion of people and organizations) is an emerging research area. Researchers are creating procedures to maintain a proper balance between maintaining information privacy and knowledge discovery by using data mining. In this paper, we initially use the prefixspan algorithm to generate sequential patterns from the medical database, and these patterns are converted into sequential rules. We then apply our proposed algorithm to evaluate these generated sequential rules according to random values. Our proposed algorithm evaluates the processed rule in terms of knowledge discovery and information loss. If the evaluated result satisfies the user-defined thresholds, our proposed algorithm releases the modified sequential rules, else further iteration is carried out until the sequential rules satisfy the user-defined threshold value. Finally, an experiment is conducted to evaluate the proposed algorithm on the basis of knowledge discovery and information loss. Key-Words: Prefix span algorithm, Sequential rule, Significant disease, Balanced constraint, Knowledge discovery, Information loss