For most data stream applications, the volume of data is too huge to be stored in permanent devices or to be thoroughly scanned more than once. It is hence recognized that approximate answers are usually sufficient, where a good approximation obtained in a timely manner is often better than the exact answer that is delayed beyond the window of opportunity. Unfortunately, this is not the case for mining frequent patterns over data streams where algorithms capable of online processing data streams do not conform strictly to a precise error guarantee. Since the quality of approximate answers is as important as their timely delivery, it is necessary to design algorithms to meet both criteria at the same time. In this paper, we propose an algori...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Frequent pattern mining from data streams is an active research topic in data mining. Existing resea...
Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rap...
In data stream applications, a good approximation obtained in a timely manner is often better ...
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredic...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
In this paper, the methods are investigate for online, frequent pattern mining of stream data, with ...
This paper introduces a new algorithm for approximate mining of frequent patterns from streams of tr...
The increasing prominence of data streams arising in a wide range of advanced applications such as f...
The increasing prominence of data streams arising in a wide range of advanced applications such as f...
International audienceMining frequent patterns on streaming data is a new challenging problem for th...
Abstract—Frequent pattern discovery over data stream is a hard problem because a continuously genera...
Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to...
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...
Frequent pattern mining from data streams is an active research topic in data mining. Existing resea...
Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rap...
In data stream applications, a good approximation obtained in a timely manner is often better ...
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredic...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
Many critical applications, like intrusion detection or stock market analysis, require a nearly imme...
In this paper, the methods are investigate for online, frequent pattern mining of stream data, with ...
This paper introduces a new algorithm for approximate mining of frequent patterns from streams of tr...
The increasing prominence of data streams arising in a wide range of advanced applications such as f...
The increasing prominence of data streams arising in a wide range of advanced applications such as f...
International audienceMining frequent patterns on streaming data is a new challenging problem for th...
Abstract—Frequent pattern discovery over data stream is a hard problem because a continuously genera...
Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to...
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...
Frequent pattern mining from data streams is an active research topic in data mining. Existing resea...
Mining frequent itemsets in a datastream proves to be a difficult problem, as itemsets arrive in rap...