A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Kiruthika, S.
- Partitioning Based Data Privacy Preservation for Overlapping Slicing Technique
Authors
1 Department of Computer Science and Engineering, Dhirajlal Gandhi College of Technology, Salem, Tamilnadu, IN
Source
Data Mining and Knowledge Engineering, Vol 7, No 10 (2015), Pagination: 329-332Abstract
Privacy preservation in data mining provides security to sensitive data in a database against unauthorized access. Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving micro data publishing. Recent work has shown that generalization loses considerable amount of information, especially for high dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi-identifying attributes and sensitive attributes. A novel technique called overlapping slicing, which partitions the data both horizontally and vertically and duplicates an attribute in more than one column. overlapping slicing preserves better data utility than generalization and can be used for membership disclosure protection.Keywords
Bucketization, Overlapping, Anonymization, Slicing.
- A Survey on Data Mining Techniques and their Comparison Approaches for Healthcare
Authors
1 Vidyasagar College of Arts and Science, Udumalpet, IN
2 PKR College for Women, Gobi, IN
Source
Data Mining and Knowledge Engineering, Vol 9, No 1 (2017), Pagination: 14-19Abstract
Data Mining (DM) has become important tool in business and related areas and its task in the healthcare field is still being explored. DM refers to mining or discovery of new information in terms of patterns or rules from vast amounts of data. To be useful, data mining must be carried out efficiently on large files and database currently, most applications of DM in healthcare can be classified into two areas: Decision Support (DS) for clinical practice, and policy development. However, it is difficult to find experimental literature in this area since a considerable amount of existing work in DM for healthcare is theoretical in nature.DM in healthcare helps in collecting heterogeneous volume of data and is stored in an organized form. The patient’s reports are maintained and analyzed in order to relate data. In view of the large amount of medical data being generated, there is growing pressure for improved methods of data analysis and knowledge discovery using appropriate data mining techniques. [1] A proper medical database created with intention mining can provide a useful resource for data mining and knowledge discovery. The terms Knowledge Discovery in Databases (KDD) and data mining are used interchangeably. KDD is the process of finding useful information, and data mining is the process for extracting knowledge (information and patterns) derived by the KDD process using algorithms .This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, Customer Relationship Management (CRM), and the detection of fraud and abuse.