Add to ORKGIn this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. A crucial part of our algorithm is the incremental maintenance of the occurrences of possibly frequent patterns using a tree sweeping technique. We give modifications of the algorithm to other online mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm
This paper presents a new learning algorithm for inducing decision trees from data streams. In thes...
This paper introduces a new algorithm for approximate mining of frequent patterns from streams of tr...
International audienceIn recent years, emerging applications introduced new constraints for data min...
Second IEEE International Conference on Data Mining (ICDM\u2702), 9-12 Dec. 2002In this paper, we st...
In this paper, the methods are investigate for online, frequent pattern mining of stream data, with ...
For most data stream applications, the volume of data is too huge to be stored in permanent devices ...
We address the problem of finding interesting substructures from a colletion of semi-structured data...
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...
Frequent pattern mining from data streams is an active research topic in data mining. Existing resea...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Abstract. Due to the dynamic nature of online information, XML doc-uments typically evolve over time...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid r...
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredic...
This paper presents a new learning algorithm for inducing decision trees from data streams. In thes...
This paper introduces a new algorithm for approximate mining of frequent patterns from streams of tr...
International audienceIn recent years, emerging applications introduced new constraints for data min...
Second IEEE International Conference on Data Mining (ICDM\u2702), 9-12 Dec. 2002In this paper, we st...
In this paper, the methods are investigate for online, frequent pattern mining of stream data, with ...
For most data stream applications, the volume of data is too huge to be stored in permanent devices ...
We address the problem of finding interesting substructures from a colletion of semi-structured data...
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...
Frequent pattern mining from data streams is an active research topic in data mining. Existing resea...
Traditional algorithms for frequent itemset discovery are designed for static data. They cannot be s...
Abstract. Due to the dynamic nature of online information, XML doc-uments typically evolve over time...
Traditional data mining techniques expect all data to be managed within some form of persistent data...
A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid r...
Data streams are usually generated in an online fashion characterized by huge volume, rapid unpredic...
This paper presents a new learning algorithm for inducing decision trees from data streams. In thes...
This paper introduces a new algorithm for approximate mining of frequent patterns from streams of tr...
International audienceIn recent years, emerging applications introduced new constraints for data min...