Qinhua Huang

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The maintenance of privacy and secrecy of information in modern communication is accomplished through steganographic technique. Spatial domain techniques are popular ones in image steganography. In this paper, we propose hybrid steganography (HDLS) which is an integration of both spatial and transform domains. The cover image as well as the payload is(More)
Positive and negative sequential patterns mining is used to discover interesting sequential patterns in a incremental transaction databases, and it is one of the essential data mining tasks widely used in various application fields. Implementation of this approach, construct tree for appended transactions (new upcoming data) and will merge this tree with(More)
Privacy-preserving data mining in distributed or grid environment is an important hot research topic in recent years. We focus on the privacy-preserving sequential pattern mining in the following situation: multiple parties, each having a private data set, wish to collaboratively discover sequential patterns on the union of the their private data sets(More)
Sequential patterns mining is an important research topic in data mining and knowledge discovery. The objective of mining sequential patterns is to find out frequent sequences based on the user-specified minimum support threshold, which implicitly assumes that all items in the data have similar frequencies. This is often not the case in real-life(More)
Sequential patterns mining is an important research topic in data mining and knowledge discovery. Traditional algorithms for mining sequential patterns are built on the binary attributes databases, which has three limitations. Firstly, it can not concern quantitative attributes; secondly, only positive sequential patterns are discovered; thirdly, it can not(More)
Association rules mining is one of the important tasks in data mining research. The key of mining association rules is to find out frequent itemsets based on the user-specified minimum support threshold, which implicitly assumes that all items in the data have similar frequencies. This is often not the case in real-life applications. If the frequencies of(More)