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排序方式: 共有10000条查询结果,搜索用时 11 毫秒
991.
In this paper, we propose an efficient scalable algorithm for mining Maximal Sequential Patterns using Sampling (MSPS). The MSPS algorithm reduces much more search space than other algorithms because both the subsequence infrequency-based
pruning and the supersequence frequency-based pruning are applied. In MSPS, a sampling technique is used to identify long
frequent sequences earlier, instead of enumerating all their subsequences. We propose how to adjust the user-specified minimum
support level for mining a sample of the database to achieve better overall performance. This method makes sampling more efficient
when the minimum support is small. A signature-based method and a hash-based method are developed for the subsequence infrequency-based
pruning when the seed set of frequent sequences for the candidate generation is too big to be loaded into memory. A prefix
tree structure is developed to count the candidate sequences of different sizes during the database scanning, and it also
facilitates the customer sequence trimming. Our experiments showed MSPS has very good performance and better scalability than
other algorithms.
Congnan Luo received the B.E. degree in Computer Science from Tsinghua University, Beijing, P.R. China, in 1997, the M.S. degree in Computer
Science from the Institute of Software, Chinese Academy of Sciences, Beijing, P.R. China, in 2000, and the Ph.D. degree in
Computer Science and Engineering from Wright State University, Dayton, OH, in 2006. Currently he is a technical staff at the
Teradata division of NCR in San Diego, CA, and his research interests include data mining, machine learning, and databases.
Soon M. Chung received the B.S. degree in Electronic Engineering from Seoul National University, Korea, in 1979, the M.S. degree in Electrical
Engineering from Korea Advanced Institute of Science and Technology, Korea, in 1981, and the Ph.D. degree in Computer Engineering
from Syracuse University, Syracuse, New York, in 1990. He is currently a Professor in the Department of Computer Science and
Engineering at Wright State University, Dayton, OH. His research interests include database, data mining, Grid computing,
text mining, XML, and parallel and distributed processing. 相似文献
992.
The increasing prominence of data streams arising in a wide range of advanced applications such as fraud detection and trend
learning has led to the study of online mining of frequent itemsets (FIs). Unlike mining static databases, mining data streams
poses many new challenges. In addition to the one-scan nature, the unbounded memory requirement and the high data arrival
rate of data streams, the combinatorial explosion of itemsets exacerbates the mining task. The high complexity of the FI mining
problem hinders the application of the stream mining techniques. We recognize that a critical review of existing techniques
is needed in order to design and develop efficient mining algorithms and data structures that are able to match the processing
rate of the mining with the high arrival rate of data streams. Within a unifying set of notations and terminologies, we describe
in this paper the efforts and main techniques for mining data streams and present a comprehensive survey of a number of the
state-of-the-art algorithms on mining frequent itemsets over data streams. We classify the stream-mining techniques into two
categories based on the window model that they adopt in order to provide insights into how and why the techniques are useful.
Then, we further analyze the algorithms according to whether they are exact or approximate and, for approximate approaches, whether they are false-positive or false-negative. We also discuss various interesting issues, including the merits and limitations in existing research and substantive areas
for future research. 相似文献
993.
Traditional clustering algorithms are inapplicable to many real-world problems where limited knowledge from domain experts
is available. Incorporating the domain knowledge can guide a clustering algorithm, consequently improving the quality of clustering.
In this paper, we propose SS-NMF: a semi-supervised non-negative matrix factorization framework for data clustering. In SS-NMF,
users are able to provide supervision for clustering in terms of pairwise constraints on a few data objects specifying whether
they “must” or “cannot” be clustered together. Through an iterative algorithm, we perform symmetric tri-factorization of the
data similarity matrix to infer the clusters. Theoretically, we show the correctness and convergence of SS-NMF. Moveover,
we show that SS-NMF provides a general framework for semi-supervised clustering. Existing approaches can be considered as
special cases of it. Through extensive experiments conducted on publicly available datasets, we demonstrate the superior performance
of SS-NMF for clustering.
相似文献
Ming DongEmail: |
994.
The profile of a graph is an integer-valued parameter defined via vertex orderings; it is known that the profile of a graph
equals the smallest number of edges of an interval supergraph. Since computing the profile of a graph is an NP-hard problem,
we consider parameterized versions of the problem. Namely, we study the problem of deciding whether the profile of a connected
graph of order n is at most n−1+k, considering k as the parameter; this is a parameterization above guaranteed value, since n−1 is a tight lower bound for the profile. We present two fixed-parameter algorithms for this problem. The first algorithm
is based on a forbidden subgraph characterization of interval graphs. The second algorithm is based on two simple kernelization
rules which allow us to produce a kernel with linear number of vertices and edges. For showing the correctness of the second
algorithm we need to establish structural properties of graphs with small profile which are of independent interest.
A preliminary version of the paper is published in Proc. IWPEC 2006, LNCS vol. 4169, 60–71. 相似文献
995.
The Individual Haplotyping MFR problem is a computational problem that, given a set of DNA sequence fragment data of an individual,
induces the corresponding haplotypes by dropping the minimum number of fragments. Bafna, Istrail, Lancia, and Rizzi proposed
an algorithm of time O(22k
m
2
n+23k
m
3) for the problem, where m is the number of fragments, n is the number of SNP sites, and k is the maximum number of holes in a fragment. When there are mate-pairs in the input data, the parameter k can be as large as 100, which would make the Bafna-Istrail-Lancia-Rizzi algorithm impracticable. The current paper introduces
a new algorithm PM-MFR of running time
, where k
1 is the maximum number of SNP sites that a fragment covers (k
1 is smaller than n), and k
2 is the maximum number of fragments that cover a SNP site (k
2 is usually about 10). Since the time complexity of the algorithm PM-MFR is not directly related to the parameter k, the algorithm solves the Individual Haplotyping MFR problem with mate-pairs more efficiently and is more practical in real
biological applications.
This research was supported in part by the National Natural Science Foundation of China under Grant Nos. 60433020 and 60773111,
the Program for New Century Excellent Talents in University No. NCET-05-0683, the Program for Changjiang Scholars and Innovative
Research Team in University No. IRT0661, and the Scientific Research Fund of Hunan Provincial Education Department under Grant
No. 06C526. 相似文献
996.
In this paper we describe a general grouping technique to devise faster and simpler approximation schemes for several scheduling
problems. We illustrate the technique on two different scheduling problems: scheduling on unrelated parallel machines with
costs and the job shop scheduling problem. The time complexity of the resulting approximation schemes is always linear in
the number n of jobs, and the multiplicative constant hidden in the O(n) running time is reasonably small and independent of the error ε.
Supported by Swiss National Science Foundation project 200020-109854, “Approximation Algorithms for Machine scheduling Through
Theory and Experiments II”.
A preliminary version of this paper appeared in the Proceedings of ESA’01. 相似文献
997.
Ali Khoshgozaran Ali Khodaei Mehdi Sharifzadeh Cyrus Shahabi 《Knowledge and Information Systems》2008,17(3):265-286
Vector data and in particular road networks are being queried, hosted and processed in many application domains such as in
mobile computing. Many client systems such as PDAs would prefer to receive the query results in unrasterized format without
introducing an overhead on overall system performance and result size. While several general vector data compression schemes
have been studied by different communities, we propose a novel approach in vector data compression which is easily integrated
within a geospatial query processing system. It uses line aggregation to reduce the number of relevant tuples and Huffman
compression to achieve a multi-resolution compressed representation of a road network database. Our experiments performed
on an end-to-end prototype verify that our approach exhibits fast query processing on both client and server sides as well
as high compression ratio.
相似文献
Cyrus ShahabiEmail: |
998.
Given a graph with edges colored Red and Blue, we study the problem of sampling and approximately counting the number of matchings with exactly k
Red edges. We solve the problem of estimating the number of perfect matchings with exactly k
Red edges for dense graphs. We study a Markov chain on the space of all matchings of a graph that favors matchings with k
Red edges. We show that it is rapidly mixing using non-traditional canonical paths that can backtrack. We show that this chain
can be used to sample matchings in the 2-dimensional toroidal lattice of any fixed size ℓ with k
Red edges, where the horizontal edges are Red and the vertical edges are Blue.
An extended abstract appeared in J.R. Correa, A. Hevia and M.A. Kiwi (eds.) Proceedings of the 7th Latin American Theoretical Informatics Symposium, LNCS 3887, pp. 190–201, Springer, 2006.
N. Bhatnagar’s and D. Randall’s research was supported in part by NSF grants CCR-0515105 and DMS-0505505.
V.V. Vazirani’s research was supported in part by NSF grants 0311541, 0220343 and CCR-0515186.
N. Bhatnagar’s and E. Vigoda’s research was supported in part by NSF grant CCR-0455666. 相似文献
999.
Huimin Zhao 《Knowledge and Information Systems》2008,15(3):321-334
In real-world classification problems, different types of misclassification errors often have asymmetric costs, thus demanding cost-sensitive learning methods that attempt to minimize average misclassification cost rather than plain error rate. Instance weighting and post hoc threshold adjusting are two major approaches to cost-sensitive classifier learning. This paper compares the effects of these two approaches on several standard, off-the-shelf classification methods. The comparison indicates that the two approaches lead to similar results for some classification methods, such as Naïve Bayes, logistic regression, and backpropagation neural network, but very different results for other methods, such as decision tree, decision table, and decision rule learners. The findings from this research have important implications on the selection of the cost-sensitive classifier learning approach as well as on the interpretation of a recently published finding about the relative performance of Naïve Bayes and decision trees. 相似文献
1000.
We study the problem of segmenting a sequence into k pieces so that the resulting segmentation satisfies monotonicity or unimodality constraints. Unimodal functions can be used
to model phenomena in which a measured variable first increases to a certain level and then decreases. We combine a well-known
unimodal regression algorithm with a simple dynamic-programming approach to obtain an optimal quadratic-time algorithm for
the problem of unimodal k-segmentation. In addition, we describe a more efficient greedy-merging heuristic that is experimentally shown to give solutions
very close to the optimal. As a concrete application of our algorithms, we describe methods for testing if a sequence behaves
unimodally or not. The methods include segmentation error comparisons, permutation testing, and a BIC-based scoring scheme.
Our experimental evaluation shows that our algorithms and the proposed unimodality tests give very intuitive results, for
both real-valued and binary data.
Niina Haiminen received the M.Sc. degree from the University of Helsinki in 2004. She is currently a Graduate Student at the Department
of Computer Science of University of Helsinki, and a Researcher at the Basic Research Unit of Helsinki Institute for Information
Technology. Her research interests include algorithms, bioinformatics, and data mining.
Aristides Gionis received the Ph.D. degree from Stanford University in 2003, and he is currently a Senior Researcher at the Basic Research
Unit of Helsinki Institute for Information Technology. His research experience includes summer internship positions at Bell
Labs, AT&T Labs, and Microsoft Research. His research areas are data mining, algorithms, and databases.
Kari Laasonen received the M.Sc. degree in Theoretical Physics in 1995 from the University of Helsinki. He is currently a Graduate Student
in Computer Science at the University of Helsinki and a Researcher at the Basic Research Unit of Helsinki Institute for Information
Technology. His research is focused on algorithms and data analysis methods for pervasive computing. 相似文献