@inproceedings{f57b8aad30f044f388a792597f8cf157,
title = "Mining top-K largest tiles in a data stream",
abstract = "Large tiles in a database are itemsets with the largest area which is defined as the itemset frequency in the database multiplied by its size. Mining these large tiles is an important pattern mining problem since tiles with a large area describe a large part of the database. In this paper, we introduce the problem of mining top-k largest tiles in a data stream under the sliding window model. We propose a candidate-based approach which summarizes the data stream and produces the top-k largest tiles efficiently for moderate window size. We also propose an approximation algorithm with theoretical bounds on the error rate to cope with large size windows. In the experiments with two real-life datasets, the approximation algorithm is up to hundred times faster than the candidate-based solution and the baseline algorithms based on the state-of-the-art solutions. We also investigate an application of large tile mining in computer vision and in emerging search topics monitoring.",
author = "T.L. Hoang and W. Pei and A. Prado and B. Jeudy and {\'E}. Fromont",
year = "2014",
doi = "10.1007/978-3-662-44851-9\_6",
language = "English",
isbn = "978-3-662-44850-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "82--97",
editor = "T. Calders and F. Esposito and E. H{\"u}llermeier and R. Meo",
booktitle = "Machine Learning and Knowledge Discovery in Databases (European Conference, ECML PKDD 2014, Nancy, France, September 15-19, 2014. Proceedings, Part II)",
address = "Germany",
note = "2014 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2014), ECML PKDD 2014 ; Conference date: 15-09-2014 Through 19-09-2014",
}