Add to ORKGThis paper introduces a new algorithm for approximate mining of frequent patterns from streams of transactions using a limited amount of memory. The proposed algorithm consists in the computation of frequent itemsets in recent data and an effective method for inferring the global support of previously infrequent itemsets. Both upper and lower bounds on the support of each pattern found are returned along with the interpolated support. An extensive experimental evaluation shows that APStream, the proposed algorithm, yields a good approximation of the exact global result considering both the set of patterns found and their support
Abstract—Frequent pattern discovery over data stream is a hard problem because a continuously genera...
Abstract. Discovering frequent patterns over event sequences is an important data mining problem. Ex...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
Abstract. Recently, the data stream, which is an unbounded sequence of data elements generated at a ...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Abstract—Frequent pattern discovery over data stream is a hard problem because a continuously genera...
Abstract. Discovering frequent patterns over event sequences is an important data mining problem. Ex...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
We study the problem of finding frequent itemsets in a continuous stream of transactions. The curren...
Abstract. Recently, the data stream, which is an unbounded sequence of data elements generated at a ...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Abstract—Frequent pattern discovery over data stream is a hard problem because a continuously genera...
Abstract. Discovering frequent patterns over event sequences is an important data mining problem. Ex...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...