An Adaptive Sliding Window Algorithm for Mining Frequent Itemsets in Computer Forensics

Data mining technology is widely utilized in the field of computer criminal forensics. The research of data mining technologies, such as frequent itemset mining, clustering etc., can effectively improve the efficiency of computer forensics. However, data items of streaming data are dynamically chang...

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Bibliographic Details
Main Authors: Xiong, Anping, Huang, Yi, Wu, Yunsong, Zhang, Jun, Long, Linbo
Format: Conference Proceeding
Language:English
Subjects:
Adaptation models
Computer security
Data mining
Data models
Forensics
Frequent itemsets
Itemsets
Microsoft Windows
Nested window model
Sliding windows
Streaming data
Online Access:Request full text
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Summary:Data mining technology is widely utilized in the field of computer criminal forensics. The research of data mining technologies, such as frequent itemset mining, clustering etc., can effectively improve the efficiency of computer forensics. However, data items of streaming data are dynamically changed along with time, which triggers off some new challenges to computer forensics. To address this issue, this paper presents an adaptive sliding window based strategy for mining the main frequent itemsets on streaming data. The key idea is to dynamically adjust the size of sliding window by exploiting the time-varying feature of streaming data, in order to satisfy the concept change that occurs in the streaming data. The experimental results show that compared with the previous work, the proposed algorithm can superiorly adapt to the time-varying feature of streaming data, and dramatically enhance time performance by reducing the data size for mining.
ISSN:2324-9013
DOI:10.1109/TrustCom/BigDataSE.2018.00246