An Efficient Probabilistic Multi Labeled Big Data Clustering Model for Privacy Preservation Using Linked Weight Optimization Model
Main Article Content
Abstract
An unsupervised analysis of the classification and clustering of data is one of the most powerful and insightful data mining approaches used in different disciplines to identify homogenous groups of objects based on similarities. In machine learning with the increased generation of data, classification continues to be a key subject. While several literary works are interested in classifying the single label, the enormous dimensions of the data require a new approach. Multi-label clustering has therefore gained considerable attention in the testing community in recent years. This method involves a data instance with different labels and it is useful for many fields, e.g. image analysis, text classification and Big Data privacy security. In this case the classification of the single label is expanded. The high dimensionality of the distributed system needs an efficient and effective data management. Multi Label Classifier divides one or more labels in a set of labels of a particular instance. Multi-label classification is one of the leading data collection methods, where a set of labels is annotated in the data collection for each single instance. In one instance, the nature of multiple labels requires more computer power than classified one-label tasks. A multi-label grouping is often simplified by the method of splitting into one label classification, which avoids the distinction between labels. A Multi-Label Big Data Clustering with Privacy Protection Probability Linked Weight Optimization (MLBDC-PP-LWO) model is provided in this paper. In this proposed work, after the identification of sensitive data from data clusters, sensitive information is protected or generalized. The models proposed are compared to existing models and the findings show that the proposed model privacy preservation levels are more than the traditional methods.
Downloads
Metrics
Article Details
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.