Research On The Mining Algorithm Based On Data Streams

With the development of information technology, the dimensions of massive databaseincrease rapidly. But there are little effective analysis and processing technology. Theemergence of the Knowledge Discovery in Databases(KDD) is made up. Data mining is animportant process of KDD. It is to discover interesting patterns from large amounts of data.Frequent pattern mining is a very important problem in data mining. Recently, large amountsof data such as web data and transaction data are accumulated in the form of data streams, andmining frequent patterns over data streams has become current research difficulty and hotspot.The paper mainly study on the mining frequent patterns over data streams, one of theimportant data stream mining problems.The detail research achievements are listed asfollows:Firstly, the research status of Data Stream Management System and data mining in datastreams is introduced. Then the paper introduce data streams mining technologies, theircharacteristics, the basic conceptions and key problems. At last, several classic algorithms ofmining frequent patterns in data streams are studied.Secondly, this paper proposes an algorithm of mining frequent patterns based on slidingwindows over data streams is developed called MFIS-stream. It can capture frequent patternsuse the data structure FIS-Tree. Meanwhile, it is convenient to update FIS-Tree because of thelast-node. The experimental results show that the MFIS-stream obtained good time efficiencyand steady, it is suitable for data streams.Finally, in order to meet the needs of the practical problems, the algorithm calledMFIWDS is developed. It can capture the frequent patterns in data streams using weightedsliding window model by the WFIS-Tree proposed. The users can adjust the number and wideof windows, determine different weights to obtain frequent patterns more satisfactory. Theexperimental test and performance comparison result show that MFIWDS is feasible.