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931.
韦良 《数字社区&智能家居》2008,(10):172-174
粗糙集理论是一种研究不精确、不确定性、处理不完备知识的数学工具,目前被广泛应用于人工智能、模式识别、机器学习、决策支持和数据挖掘等领域。该文通过介绍粗糙集理论及特点,叙述了粗糙集理论在各领域的应用发展情况,并且展望了其未来发展趋势。 相似文献
932.
LIU Jin-zhong 《数字社区&智能家居》2008,(12)
挖掘关联规则是数据挖掘领域的一个重要研究方向,本文首先介绍了一种基于层次的Apriori算法和一种基于搜索算法的QAIS算法,通过二者的比较,指出了QAIS算法中的优点以及不足之处。然后有针对性的提出了解决的方案,形成了ImprovedQAIS算法。 相似文献
933.
Minimizing False Positives of a Decision Tree Classifier for Intrusion Detection on the Internet 总被引:1,自引:0,他引:1
Satoru Ohta Ryosuke Kurebayashi Kiyoshi Kobayashi 《Journal of Network and Systems Management》2008,16(4):399-419
Machine learning or data mining technologies are often used in network intrusion detection systems. An intrusion detection
system based on machine learning utilizes a classifier to infer the current state from the observed traffic attributes. The
problem with learning-based intrusion detection is that it leads to false positives and so incurs unnecessary additional operation
costs. This paper investigates a method to decrease the false positives generated by an intrusion detection system that employs
a decision tree as its classifier. The paper first points out that the information-gain criterion used in previous studies
to select the attributes in the tree-constructing algorithm is not effective in achieving low false positive rates. Instead
of the information-gain criterion, this paper proposes a new function that evaluates the goodness of an attribute by considering
the significance of error types. The proposed function can successfully choose an attribute that suppresses false positives
from the given attribute set and the effectiveness of using it is confirmed experimentally. This paper also examines the more
trivial leaf rewriting approach to benchmark the proposed method. The comparison shows that the proposed attribute evaluation
function yields better solutions than the leaf rewriting approach.
相似文献
Satoru OhtaEmail: |
934.
935.
Jimeng Sun Charalampos E. Tsourakakis Evan Hoke Christos Faloutsos Tina Eliassi-Rad 《Data mining and knowledge discovery》2008,17(1):111-128
Data stream values are often associated with multiple aspects. For example each value observed at a given time-stamp from environmental sensors may have an associated type (e.g., temperature,
humidity, etc.) as well as location. Time-stamp, type and location are the three aspects, which can be modeled using a tensor
(high-order array). However, the time aspect is special, with a natural ordering, and with successive time-ticks having usually
correlated values. Standard multiway analysis ignores this structure. To capture it, we propose 2 Heads Tensor Analysis (2-heads), which provides a qualitatively different treatment on time. Unlike most existing approaches that use a PCA-like
summarization scheme for all aspects, 2-heads treats the time aspect carefully. 2-heads combines the power of classic multilinear
analysis with wavelets, leading to a powerful mining tool. Furthermore, 2-heads has several other advantages as well: (a)
it can be computed incrementally in a streaming fashion, (b) it has a provable error guarantee and, (c) it achieves significant
compression ratio against competitors. Finally, we show experiments on real datasets, and we illustrate how 2-heads reveals
interesting trends in the data. This is an extended abstract of an article published in the Data Mining and Knowledge Discovery
journal. 相似文献
936.
Data co-clustering refers to the problem of simultaneous clustering of two data types. Typically, the data is stored in a
contingency or co-occurrence matrix C where rows and columns of the matrix represent the data types to be co-clustered. An entry C
ij
of the matrix signifies the relation between the data type represented by row i and column j. Co-clustering is the problem of deriving sub-matrices from the larger data matrix by simultaneously clustering rows and
columns of the data matrix. In this paper, we present a novel graph theoretic approach to data co-clustering. The two data
types are modeled as the two sets of vertices of a weighted bipartite graph. We then propose Isoperimetric Co-clustering Algorithm
(ICA)—a new method for partitioning the bipartite graph. ICA requires a simple solution to a sparse system of linear equations
instead of the eigenvalue or SVD problem in the popular spectral co-clustering approach. Our theoretical analysis and extensive
experiments performed on publicly available datasets demonstrate the advantages of ICA over other approaches in terms of the
quality, efficiency and stability in partitioning the bipartite graph. 相似文献
937.
Prefetching in Content Distribution Networks via Web Communities Identification and Outsourcing 总被引:1,自引:0,他引:1
Antonis Sidiropoulos George Pallis Dimitrios Katsaros Konstantinos Stamos Athena Vakali Yannis Manolopoulos 《World Wide Web》2008,11(1):39-70
Content distribution networks (CDNs) improve scalability and reliability, by replicating content to the “edge” of the Internet.
Apart from the pure networking issues of the CDNs relevant to the establishment of the infrastructure, some very crucial data
management issues must be resolved to exploit the full potential of CDNs to reduce the “last mile” latencies. A very important
issue is the selection of the content to be prefetched to the CDN servers. All the approaches developed so far, assume the
existence of adequate content popularity statistics to drive the prefetch decisions. Such information though, is not always
available, or it is extremely volatile, turning such methods problematic. To address this issue, we develop self-adaptive
techniques to select the outsourced content in a CDN infrastructure, which requires no apriori knowledge of request statistics.
We identify clusters of “correlated” Web pages in a site, called Web site communities, and make these communities the basic outsourcing unit. Through a detailed simulation environment, using both real and synthetic
data, we show that the proposed techniques are very robust and effective in reducing the user-perceived latency, performing
very close to an unfeasible, off-line policy, which has full knowledge of the content popularity. 相似文献
938.
HONG Tzung-Pei HUANG Tzu-Jung CHANG Chao-Sheng 《通讯和计算机》2008,5(12):1-9
Developing an efficient algorithm that can maintain discovered information as a database changes is quite important in data mining. Many proposed algorithms focused on a single level, and did not utilize previously mined information in incrementally growing databases. In the past, we proposed an incremental mining algorithm for maintenance of multiple-level association rules as new transactions were inserted. Deletion of records in databases is, however, commonly seen in real-world applications. In this paper, we thus attempt to extend our previous approach to solve this issue. The concept of pre-large itemsets is used to reduce the need for rescanning original databases and to save maintenance costs. A pre-large itemset is not truly large, but promises to be large in the future. A lower support threshold and an upper support threshold are used to realize this concept. The two user-specified upper and lower support thresholds make the pre-large itemsets act as a gap to avoid small itemsets becoming large in the updated database when transactions are deleted. A new algorithm is thus proposed based on the concept to maintain discovered multiple-level association rules for deletion of records. The proposed algorithm doesn't need to rescan the original database until a number of records have been deleted. It can thus save much maintenance time. 相似文献
939.
This study proposed a novel PSO–SVM model that hybridized the particle swarm optimization (PSO) and support vector machines (SVM) to improve the classification accuracy with a small and appropriate feature subset. This optimization mechanism combined the discrete PSO with the continuous-valued PSO to simultaneously optimize the input feature subset selection and the SVM kernel parameter setting. The hybrid PSO–SVM data mining system was implemented via a distributed architecture using the web service technology to reduce the computational time. In a heterogeneous computing environment, the PSO optimization was performed on the application server and the SVM model was trained on the client (agent) computer. The experimental results showed the proposed approach can correctly select the discriminating input features and also achieve high classification accuracy. 相似文献
940.