Mining for empty spaces in large data sets
TLDR
In this article, the problem of finding all maximal empty rectangles in large, two-dimensional data sets was considered and a scalable algorithm for finding all such rectangles was introduced, with a single scan over a sorted data set and requires only a small bounded amount of memory.About:
This article is published in Theoretical Computer Science.The article was published on 2003-03-14 and is currently open access. It has received 55 citations till now. The article focuses on the topics: Search problem & Query optimization.read more
Citations
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Journal ArticleDOI
A rank-by-feature framework for interactive exploration of multidimensional data
Jinwook Seo,Ben Shneiderman +1 more
TL;DR: A set of principles and a novel rank-by-feature framework that could enable users to better understand distributions in one (1D) or two dimensions (2D) and discover relationships, clusters, gaps, outliers, and other features and implemented in the Hierarchical Clustering Explorer.
Journal ArticleDOI
Module placement for fault-tolerant microfluidics-based biochips
Fei Su,Krishnendu Chakrabarty +1 more
TL;DR: A simulated annealing-based technique for module placement in “digital” droplet-based microfluidic biochips is presented, which not only addresses chip area, but also considers fault tolerance, which allows a micro fluidic module to be relocated elsewhere in the system when a single cell is detected to be faulty.
Proceedings ArticleDOI
An efficient algorithm for finding empty space for online FPGA placement
Manish Handa,Ranga Vemuri +1 more
TL;DR: An algorithm that finds empty area as a list of overlapping maximal rectangles using an innovative representation of the FPGA is presented, able to predict possible locations of the maximal empty rectangles.
Proceedings ArticleDOI
Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams
Elsa Loekito,James Bailey +1 more
TL;DR: A performance study demonstrates the ZBDD technique is highly scalable, substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes.
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On the Largest Empty Axis-Parallel Box Amidst n Points
Adrian Dumitrescu,Minghui Jiang +1 more
TL;DR: In this article, a (1−e)-approximation algorithm for the problem of finding an empty axis-aligned box whose volume is at least (1 − e) of the maximum was given.
References
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Book
Computers and Intractability: A Guide to the Theory of NP-Completeness
TL;DR: The second edition of a quarterly column as discussed by the authors provides a continuing update to the list of problems (NP-complete and harder) presented by M. R. Garey and myself in our book "Computers and Intractability: A Guide to the Theory of NP-Completeness,” W. H. Freeman & Co., San Francisco, 1979.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Proceedings ArticleDOI
BIRCH: an efficient data clustering method for very large databases
TL;DR: Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) as discussed by the authors is a data clustering method that is especially suitable for very large databases.
Journal ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant transactions in a large database of customer transactions that consists of items purchased by a customer in a visit.
Proceedings ArticleDOI
Association rules over interval data
Renée J. Miller,Y. Yang +1 more
TL;DR: An algorithm for mining association rules under the new definition of interest for association rules that takes into account the semantics of interval data is developed and the experience using the algorithm on large real-life datasets is overview.