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Fast mining of high dimensional expressive contrast patterns using zerosuppressed binary decision diagrams
 In KDD
, 2006
"... Patterns of contrast are a very important way of comparing multidimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challeng ..."
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Cited by 18 (3 self)
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Patterns of contrast are a very important way of comparing multidimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challenging when the number of dimensions is large. This paper describes a new technique for mining several varieties of contrast pattern, based on the use of ZeroSuppressed Binary Decision Diagrams (ZBDDs), a powerful data structure for manipulating sparse data. We study the mining of both simple contrast patterns, such as emerging patterns, and more novel and complex contrasts, which we call disjunctive emerging patterns. A performance study demonstrates our 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.
A Compressed BreadthFirst Search for Satisfiability
 Proc. 4th Workshop on Algorithm Engineering and Experiments
, 2002
"... Leading algorithms for Boolean satisfiability (SAT) are based on either a depthfirst tree traversal of the search space (the DLL procedure [6]) or resolution (the DP procedure [7]). In this work we introduce a variant of BreadthFirst Search (BFS) based on the ability of ZeroSuppressed Binary De ..."
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Cited by 17 (3 self)
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Leading algorithms for Boolean satisfiability (SAT) are based on either a depthfirst tree traversal of the search space (the DLL procedure [6]) or resolution (the DP procedure [7]). In this work we introduce a variant of BreadthFirst Search (BFS) based on the ability of ZeroSuppressed Binary Decision Diagrams (ZDDs) to compactly represent sparse or structured collections of subsets.
Using ZBDDs in pointsto analysis
 In Workshops on Languages and Compilers for Parallel Computing (LCPC
, 2007
"... Abstract. Binary Decision Diagrams (BDDs) have recently become widely accepted as a spaceefficient method of representing relations in pointsto analyses. When BDDs are used to represent relations, each element of a domain is assigned a bit pattern to represent it, but not every bit pattern represe ..."
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Cited by 4 (0 self)
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Abstract. Binary Decision Diagrams (BDDs) have recently become widely accepted as a spaceefficient method of representing relations in pointsto analyses. When BDDs are used to represent relations, each element of a domain is assigned a bit pattern to represent it, but not every bit pattern represents an element. The circuit design, model checking, and verification communities have achieved significant reductions in BDD sizes using ZeroSuppressed BDDs (ZBDDs) to avoid the overhead of these don’tcare bit patterns. We adapt BDDbased program analyses to use ZBDDs instead of BDDs. Our experimental evaluation studies the space requirements of ZBDDs for both contextinsensitive and contextsensitive program analyses and shows that ZBDDs can greatly reduce the space requirements for expensive contextsensitive pointsto analysis. Using ZBDDs to reduce the size of the relations allows a compiler or other software analysis tools to analyze larger programs with greater precision. We also provide a metric that can be used to estimate whether ZBDDs will be more compact than BDDs for a given analysis. 1
Resolution Cannot Polynomially Simulate CompressedBFS
 Ann. of Math. and A.I
, 2003
"... Many algorithms for Boolean satisfiability (SAT) work within the framework of resolution as a proof system, and thus on unsatisfiable instances they can be viewed as attempting to find proofs by resolution. However it has been known since the 1980s that every resolution proof of the pigeonhole princ ..."
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Cited by 3 (0 self)
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Many algorithms for Boolean satisfiability (SAT) work within the framework of resolution as a proof system, and thus on unsatisfiable instances they can be viewed as attempting to find proofs by resolution. However it has been known since the 1980s that every resolution proof of the pigeonhole principle (PHP n ), suitably encoded as a CNF instance, includes exponentially many steps [1]. Therefore SAT solvers based upon the DLL procedure [2] or the DP procedure [3] must take exponential time. Polynomialsized proofs of the pigeonhole principle exist for different proof systems, but generalpurpose SAT solvers often remain confined to resolution. This result is in correlation with empirical evidence. Previously, we introduced...
Are Zerosuppressed Binary Decision Diagrams Good for Mining Frequent Patterns in High Dimensional Datasets? Abstract
"... Mining frequent patterns such as frequent itemsets is a core operation in many important data mining tasks, such as in association rule mining. Mining frequent itemsets in highdimensional datasets is challenging, since the search space is exponential in the number of dimensions and the volume of pa ..."
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Cited by 3 (2 self)
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Mining frequent patterns such as frequent itemsets is a core operation in many important data mining tasks, such as in association rule mining. Mining frequent itemsets in highdimensional datasets is challenging, since the search space is exponential in the number of dimensions and the volume of patterns can be huge. Many of the stateoftheart techniques rely upon the use of prefix trees (e.g. FPtrees) which allow nodes to be shared among common prefix paths. However, the scalability of such techniques may be limited when handling high dimensional datasets. The purpose of this paper is to analyse the behaviour of mining frequent itemsets when instead of a tree data structure, a canonical directed acyclic graph namely Zero Suppressed Binary Decision Diagram (ZBDD) is used. Due to its compactness and ability to promote node reuse, ZBDD has proven very effective in other areas of computer science, such as boolean SAT solvers. In this paper, we show how ZBDDs can be used to mine frequent itemsets (and their common varieties). We also introduce a weighted variant of ZBDD which allows a more efficient mining algorithm to be developed. We provide an experimental study concentrating on high dimensional biological datasets, and identify indicative situations where a ZBDD technology can be superior over the prefix tree based technique.
Nearest Neighbour Search with ZeroSuppressed Decision Diagram for Text Retrieval
"... Exact NNS on text document becomes very expensive for its high dimensionality and the volumes. We demonstrate Exact NNS on text can be performed in linear time by using ZeroSuppressed Binary Decision Diagram (ZDD). We also demonstrate ZDD combined with Multihash technique can perform Approximate N ..."
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Exact NNS on text document becomes very expensive for its high dimensionality and the volumes. We demonstrate Exact NNS on text can be performed in linear time by using ZeroSuppressed Binary Decision Diagram (ZDD). We also demonstrate ZDD combined with Multihash technique can perform Approximate NNS on text with sublinear time. The exact NNS Naïve NNZDD model query time is order of magnitude faster than exhaustive NN for text, and approximate NNS using ZDD Multihash’s query time is closer to LSH with less build time and better accuracy. Acknowledgements Thanks Dr. Scott for sharing his view and precious time to honestly guide a rookie. Thanks Dr. Weifa for sharing his project experience and living tips in Canberra.