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Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

by Jiawei Han, Jian Pei, Yiwen Yin, Runying Mao - DATA MINING AND KNOWLEDGE DISCOVERY , 2004
"... Mining frequent patterns in transaction databases, time-series databases, and many other kinds of databases has been studied popularly in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still co ..."
Abstract - Cited by 1752 (64 self) - Add to MetaCart
costly, especially when there exist a large number of patterns and/or long patterns. In this study, we propose a novel frequent-pattern tree (FP-tree) structure, which is an extended prefix-tree structure for storing compressed, crucial information about frequent patterns, and develop an efficient FP-tree

Efficient Frequent Pattern Tree Construction

by D. Bujji Babu, R. Siva Rama Prasad
"... Association rule learning is a popular and well researched technique for discovering interesting relations between variables in large databases in the area of data mining. The association rules are a part of intelligent systems. Association rules are usually required to satisfy a user-specified mini ..."
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-specified minimum support and a user-specified minimum confidence at the same time. Apriori and FP-Growth algorithms are very familiar algorithms for association rule mining. In this paper we are more concentrated on the Construction of efficient frequent pattern trees. Here, we present the novel frequent pattern

gSpan: Graph-Based Substructure Pattern Mining

by Xifeng Yan, Jiawei Han , 2002
"... We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
Abstract - Cited by 650 (34 self) - Add to MetaCart
We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs

Suffix arrays: A new method for on-line string searches

by Udi Manber, Gene Myers , 1991
"... A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees is that ..."
Abstract - Cited by 835 (0 self) - Add to MetaCart
A new and conceptually simple data structure, called a suffix array, for on-line string searches is intro-duced in this paper. Constructing and querying suffix arrays is reduced to a sort and search paradigm that employs novel algorithms. The main advantage of suffix arrays over suffix trees

Comparison of discrimination methods for the classification of tumors using gene expression data

by Sandrine Dudoit, Jane Fridlyand, Terence P. Speed - JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION , 2002
"... A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells for thousand ..."
Abstract - Cited by 770 (6 self) - Add to MetaCart
A reliable and precise classification of tumors is essential for successful diagnosis and treatment of cancer. cDNA microarrays and high-density oligonucleotide chips are novel biotechnologies increasingly used in cancer research. By allowing the monitoring of expression levels in cells

Locality-sensitive hashing scheme based on p-stable distributions

by Mayur Datar, Piotr Indyk - In SCG ’04: Proceedings of the twentieth annual symposium on Computational geometry , 2004
"... inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate ..."
Abstract - Cited by 521 (8 self) - Add to MetaCart
inÇÐÓ�Ò We present a novel Locality-Sensitive Hashing scheme for the Approximate Nearest Neighbor Problem underÐÔnorm, based onÔstable distributions. Our scheme improves the running time of the earlier algorithm for the case of theÐnorm. It also yields the first known provably efficient approximate

Privacy-Preserving Data Mining

by Rakesh Agrawal , Ramakrishnan Srikant , 2000
"... A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models with ..."
Abstract - Cited by 844 (3 self) - Add to MetaCart
without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from tredning data in which the values of individual records have been perturbed. The resulting data records look very different from the original records

Nonparametric model for background subtraction

by Ahmed Elgammal, David Harwood, Larry Davis - in ECCV ’00 , 2000
"... Abstract. Background subtraction is a method typically used to seg-ment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model can ..."
Abstract - Cited by 545 (17 self) - Add to MetaCart
Abstract. Background subtraction is a method typically used to seg-ment moving regions in image sequences taken from a static camera by comparing each new frame to a model of the scene background. We present a novel non-parametric background model and a background subtraction approach. The model

Similarity search in high dimensions via hashing

by Aristides Gionis, Piotr Indyk, Rajeev Motwani , 1999
"... The nearest- or near-neighbor query problems arise in a large variety of database applications, usually in the context of similarity searching. Of late, there has been increasing interest in building search/index structures for performing similarity search over high-dimensional data, e.g., image dat ..."
Abstract - Cited by 641 (10 self) - Add to MetaCart
to 20, searching in k-d trees and related structures involves the inspection of a large fraction of the database, thereby doing no better than brute-force linear search. It has been suggested that since the selection of features and the choice of a distance metric in typical applications is rather

Dynamic Bayesian Networks: Representation, Inference and Learning

by Kevin Patrick Murphy , 2002
"... Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have bee ..."
Abstract - Cited by 770 (3 self) - Add to MetaCart
sequential data. In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T
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