A Privacy Preserved Data Mining Approach Based on k-Partite Graph Theory
نویسندگان
چکیده
منابع مشابه
Graph Mining based on a Data Partitioning Approach
Existing graph mining algorithms typically assume that the dataset can fit into main memory. As many large graph datasets cannot satisfy this condition, truly scalable graph mining remains a challenging computational problem. In this paper, we present a new horizontal data partitioning framework for graph mining. The original dataset is divided into fragments, then each fragment is mined indivi...
متن کاملa swift heuristic algorithm base on data mining approach for the Periodic Vehicle Routing Problem: data mining approach
periodic vehicle routing problem focuses on establishing a plan of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. This paper presents a new effective heuristic algorithm based on data mining tools for periodic vehicle routing problem (PVRP). The related results of proposed algorithm are compared with the resu...
متن کاملCustomer Retention Based on the Number of Purchase: A Data Mining Approach
Purpose: this study wants to find any relationship between the numbers of purchase and the income the customer brings to the company. The attempt is to find those customers who buy more than one life insurance policy and represent the signs of good payments at the same time by the help of data mining tools. Design/ methodology/ approach: the approach of this research is to use data mining tools...
متن کاملGraph-Based Data Mining
Graph-based data mining represents a collection of techniques for mining the relational aspects of data represented as a graph. Two major approaches to graphbased data mining are frequent subgraph mining and graph-based relational learning. This article will focus on one particular approach embodied in the Subdue system, along with recent advances in graph-based supervised learning, graph-based...
متن کاملPrivacy-Preserved Big Data Analysis Based on Asymmetric Imputation Kernels
This study presents an efficient approach for incomplete data classification, where the entries of samples are missing or masked due to privacy preservation. To deal with these incomplete data, a new kernel function with asymmetric intrinsic mappings is proposed in this study. Such a new kernel uses three-side similarities for kernel matrix formation. The similarity between a testing instance a...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Computer Science
سال: 2015
ISSN: 1877-0509
DOI: 10.1016/j.procs.2015.06.049