Add to ORKGSequential sampling algorithms have recently attracted interest as a way to design scalable algorithms for Data mining and KDD processes. In this paper, we identify an elementary sequential sampling task (estimation from examples), from which one can derive many other tasks appearing in practice. We present a generic algorithm to solve this task and an analysis of its correctness and running time that is simpler and more intuitive than those existing in the literature. For two specific tasks, frequency and advantage estimation, we derive lower bounds on running time in addition to the general upper bounds
Abstract. Consistent sampling is a technique for specifying, in small space, a subset S of a potenti...
Frequent Pattern mining is modified by Sequential Pattern Mining to consider time regularity which i...
Consistent sampling is a technique for specifying, in small space, a subset S of a potentially large...
Sequential sampling algorithms have recently attracted interest as a way to design scalable algorith...
Sequential sampling algorithms have recently attracted interest as a way to design scalable algorith...
AbstractA sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that ...
Scalability is a key requirement for any KDD and data mining algorithm, and one of the biggest resea...
Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-be...
One of the biggest research challenges in KDD and Data Mining is to develop methods that scale up w...
International audienceIn the last years, the field of data mining has undergone extensive work on pa...
Sequential pattern mining is a fundamental data mining task with application in several domains. We ...
The goal of data mining algorithm is to discover useful information embedded in large databases. Fre...
Abstract:- In this paper, we propose a new solution for dynamic task scheduling in distributed envir...
http://deepblue.lib.umich.edu/bitstream/2027.42/3366/5/bab0803.0001.001.pdfhttp://deepblue.lib.umich...
We examine several methods for drawing a sequential random sample of n records from a file containin...
Abstract. Consistent sampling is a technique for specifying, in small space, a subset S of a potenti...
Frequent Pattern mining is modified by Sequential Pattern Mining to consider time regularity which i...
Consistent sampling is a technique for specifying, in small space, a subset S of a potentially large...
Sequential sampling algorithms have recently attracted interest as a way to design scalable algorith...
Sequential sampling algorithms have recently attracted interest as a way to design scalable algorith...
AbstractA sequential sampling algorithm or adaptive sampling algorithm is a sampling algorithm that ...
Scalability is a key requirement for any KDD and data mining algorithm, and one of the biggest resea...
Many discovery problems, e.g., subgroup or association rule discovery, can naturally be cast as n-be...
One of the biggest research challenges in KDD and Data Mining is to develop methods that scale up w...
International audienceIn the last years, the field of data mining has undergone extensive work on pa...
Sequential pattern mining is a fundamental data mining task with application in several domains. We ...
The goal of data mining algorithm is to discover useful information embedded in large databases. Fre...
Abstract:- In this paper, we propose a new solution for dynamic task scheduling in distributed envir...
http://deepblue.lib.umich.edu/bitstream/2027.42/3366/5/bab0803.0001.001.pdfhttp://deepblue.lib.umich...
We examine several methods for drawing a sequential random sample of n records from a file containin...
Abstract. Consistent sampling is a technique for specifying, in small space, a subset S of a potenti...
Frequent Pattern mining is modified by Sequential Pattern Mining to consider time regularity which i...
Consistent sampling is a technique for specifying, in small space, a subset S of a potentially large...