A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing ...A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing time. The processing time is greatly influenced bythe amount of information that should be maintained. This issue becomes more serious in findingfrequent itemsets or frequency counting over an online transactional data stream since there can bea large number of itemsets to be monitored. We have proposed a method called the estDec method forfinding frequent itemsets over an online data stream. In order to reduce the number of monitoreditemsets in this method, monitoring the count of an itemset is delayed until its support is largeenough to become a frequent itemset in the near future. For this purpose, the count of an itemsetshould be estimated. Consequently, how to estimate the count of an itemset is a critical issue inminimizing memory usage as well as processing time. In this paper, the effects of various countestimation methods for finding frequent itemsets are analyzed in terms of mining accuracy, memoryusage and processing time.展开更多
文摘A data stream is a massive unbounded sequence of data elements continuouslygenerated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice thecorrectness of their results for fast processing time. The processing time is greatly influenced bythe amount of information that should be maintained. This issue becomes more serious in findingfrequent itemsets or frequency counting over an online transactional data stream since there can bea large number of itemsets to be monitored. We have proposed a method called the estDec method forfinding frequent itemsets over an online data stream. In order to reduce the number of monitoreditemsets in this method, monitoring the count of an itemset is delayed until its support is largeenough to become a frequent itemset in the near future. For this purpose, the count of an itemsetshould be estimated. Consequently, how to estimate the count of an itemset is a critical issue inminimizing memory usage as well as processing time. In this paper, the effects of various countestimation methods for finding frequent itemsets are analyzed in terms of mining accuracy, memoryusage and processing time.
