The well-known privacy-preserved data mining modifies existing data mining techniques to randomized data. In this paper, we investigate data mining as a technique for masking data, therefore, termed data mining based privacy protection. This approach incorporates partially the requirement of a targeted data mining task into the process of masking data so that essential structure is preserved in the masked data. The idea is simple but novel: we explore the data generalization concept from data mining as a way to hide detailed information, rather than discover trends and patterns. Once the data is masked, standard data mining techniques can be applied without modification. Our work demonstrated another positive use of data mining technology: not only can it discover useful patterns, but also mask private information. We consider the following privacy problem: a data holder wants to release a version of data for building classification models, but wants to protect against linking the released data to an external source for inferring sensitive information. We adapt an iterative bottom-up generalization from data mining to generalize the data. The generalized data remains useful to classification but becomes difficult to link to other sources. The generalization space is specified by a hierarchical structure of generalizations. A key is identifying the best generalization to climb up the hierarchy at each iteration. Enumerating all candidate generalizations is impractical. We present a scalable solution that examines at most one generalization in each iteration for each attribute involved in the linking.

In this paper, the ranking performance of six most popular and easily comprehensive multicriteria decision-making (MCDM) methods, i.e. weighted sum method (WSM), weighted product method (WPM), weighted aggregated sum product assessment (WASPAS) method, multi-objective optimization on the basis of ratio analysis and reference point approach (MOORA) method, and multiplicative form of MOORA method (MULTIMOORA) is investigated using two real time industrial robot selection problems. Both single dimensional and high dimensional weight sensitivity analyses are performed to study the effects of weight variations of the most important as well as the most critical criterion on the ranking stability of all the six considered MCDM methods. The identified local weight stability interval indicates the range of weights within which the rank of the best alternative remains unaltered, whereas, the global weight stability interval determines the range of weights within which the overall rank order of all the alternatives remains unaffected. It is observed that for both the problems, multiplicative form of MOORA is the most robust method being least affected by the changing weights of the most important and the most critical criteria.

ABSTRACT

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To sustain in the modern era of rapid manufacturing development, it becomes necessary to generate complex shapes on materials which are highly temperature and corrosion resistant, hard to machine, and have high strength-toweight ratio. Generation of complex shapes on those materials using conventional machining processes ultimately affects surface finish, material removal rate, accuracy, cost, safety etc. Non-traditional machining (NTM) processes have the capability to machine those advanced engineering materials with satisfactory results. But, selection of the most appropriate NTM process for a particular machining application is often a complicated task. Case-based reasoning (CBR), a domain of artificial intelligence, is a paradigm for reasoning new problems from the past experience. In CBR, a memory model is assumed for representing, indexing and organizing past similar cases, and a process model is supposed for retrieving and modifying the past cases and assimilating the new ones. This paper primarily focuses on the application of CBR approach for NTM process selection. Based on different process characteristics and process parameter values, the past similar cases are retrieved and reused to solve a current NTM process selection problem. For this, a software prototype is developed and three real time examples are cited to illustrate the application potentiality of CBR system.