Mining Frequent Items in Uncertain Dataset

Because of uncertainty data, traditional algorithm of mining frequent items in certain dataset is difficult to apply to uncertain dataset. Considering characteristics of uncertain data, an improved vertical mining algorithm to find frequent items in uncertain dataset was proposed with the algorithm thought of classic vertical algorithm-Eclat in certain dataset. The improved algorithm merged TID field and corresponding probability field into probability vector. During the expansion of itemset and probability vector, itemset tree was established, and the support of candidate itemsets was calculated by means of vector operations. The improved algorithm is proved to be feasible and efficient according to experimental comparison and analysis.

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Data Mining Algorithm of Frequent Probability Item Based on Sliding Window

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.3268 ◽

2014 ◽

Vol 602-605 ◽

pp. 3268-3271

Author(s):

Zhi Zhang ◽

Qi Fu

Keyword(s):

Data Stream ◽

Uncertain Data ◽

Sliding Window ◽

Relevant Information ◽

Data Mining Algorithm ◽

Performance Requirement ◽

Frequent Item ◽

Mining Algorithm ◽

Frequent Items ◽

And Performance

In order to meet the uncertain data stream mining demand in large dynamic database, a frequent probability item mining algorithm was proposed base on sliding window. The mass data in the database was regarded as a data stream. In the window model of data stream, the frequent item set was extracted according to the probability frequency distribution information of data. Compared to the traditional algorithm, the mining environmental constraints of the certain data stream was overcome, the defect that the relevant information was easy to lose was improved. The true information of data was reflected fully, and the most accurate frequent item was minded. Simulation result shows that the new algorithm can mine the frequent items accurately, and the accuracy rate is higher than the traditional method. It can process the data quickly. It provides effective strategy for analyzing the large database, and it can meet the memory requirement and performance requirement in database analysis and mining.

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The Research of Improved Apriori Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.263-266.2179 ◽

2012 ◽

Vol 263-266 ◽

pp. 2179-2184 ◽

Cited By ~ 4

Author(s):

Zhen Yun Liao ◽

Xiu Fen Fu ◽

Ya Guang Wang

Keyword(s):

Association Rule ◽

Association Rule Mining ◽

Apriori Algorithm ◽

Rule Mining ◽

Frequent Item ◽

Mining Algorithm ◽

The Times ◽

Frequent Item Sets ◽

Candidate Item ◽

Improved Algorithm

The first step of the association rule mining algorithm Apriori generate a lot of candidate item sets which are not frequent item sets, and all of these item sets cost a lot of system spending. To solve this problem，this paper presents an improved algorithm based on Apriori algorithm to improve the Apriori pruning step. Using this method, the large number of useless candidate item sets can be reduced effectively and it can also reduce the times of judge whether the item sets are frequent item sets. Experimental results show that the improved algorithm has better efficiency than classic Apriori algorithm.

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A Novel Edge Detection Algorithm for Medical Image

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.151.653 ◽

2012 ◽

Vol 151 ◽

pp. 653-656

Author(s):

Zhan Chun Ma ◽

Xiao Mei Ning

Keyword(s):

Edge Detection ◽

Medical Image ◽

Detection Algorithm ◽

Canny Operator ◽

Edge Detection Algorithm ◽

Image Edge Detection ◽

Traditional Algorithm ◽

Image Edge ◽

Improved Algorithm

CANNY operator had widely usage for edge detection; however it also had certain deficiencies. So the traditional CANNY operator about this is improved and puts forward a kind of new algorithm used for image edge detection. Compared improved algorithm with traditional algorithm for edge detection, simulations shows that new algorithm is more effective for image edge detection and the clearer detection result is obtained.

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The Research of Improved Apriori Mining Algorithm in Bank Customer Segmentation

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.760-762.2244 ◽

2013 ◽

Vol 760-762 ◽

pp. 2244-2249 ◽

Cited By ~ 1

Author(s):

Gong Xin Yang

Keyword(s):

Customer Service ◽

Customer Segmentation ◽

Decision Makers ◽

Mining Algorithm ◽

Optimal Value ◽

Segmentation Accuracy ◽

Traditional Algorithm ◽

Customer Classification ◽

Customers Segmentation ◽

Service Strategies

The This paper studies bank customers segmentation problem. Improved Apriori mining algorithm is a kind of data mining technology which is an important method in bank customers segmentation. In practical application, the traditional algorithm has shortcomings of the initial values sensitive and easy to fall into local optimal value, which will lead to low accuracy rate of silver class customer classification. According to the shortcomings of traditional algorithm, this paper puts forward a bank customer segmentation method based on improved Apriori mining algorithm in order to improve the bank customer segmentation accuracy. Experimental results show that the algorithm can effectively overcome the traditional algorithms shortcomings of easy to fall into local optimal value, improve the customer classification accuracy, make mining results more reasonable, lay down different customer service strategies for different client base, improve effective reference opinions of bank decision makers, and bring more benefits for the bank.

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Efficient Mining Algorithm of Frequent Itemsets for Uncertain Data Streams

2016 9th International Symposium on Computational Intelligence and Design (ISCID) ◽

10.1109/iscid.2016.2110 ◽

2016 ◽

Author(s):

Wang Qianqian ◽

Liu Fang-Ai

Keyword(s):

Data Streams ◽

Uncertain Data ◽

Frequent Itemsets ◽

Mining Algorithm ◽

Uncertain Data Streams

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Tracking of Traffic Monitoring Targets in Complicated Traffic Scene Based on MeanShift Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.744-746.2012 ◽

2015 ◽

Vol 744-746 ◽

pp. 2012-2018

Author(s):

Jian You Zhao ◽

Jian Cui

Keyword(s):

Spatial Distribution ◽

Recognition Accuracy ◽

Traffic Monitoring ◽

Moving Targets ◽

Weighting Functions ◽

Traditional Algorithm ◽

Monitoring Area ◽

Centroid Position ◽

Better Than ◽

Improved Algorithm

To avoid the the interference of busy backgrounds when tracking, detecting and recognizing moving targets in complicated traffic scene, an improved algorithm is proposed on the basis of the original MeanShift algorithm which use different colors of the centroid positions to identify the target. MeanShift algorithm can be used to calcucte the centroid position of each color in the monitoring area. Then the centroid positon of every color in every frame can be identified by analyzing spatial distribution and iteration. At last, establish weighting functions to increase the recognition accuracy so as to recognize and track the targets in complicated traffic scene. Experiments have shown that the improved algorithm is better than the traditional algorithm in identifying and tracking moving targets in the monitoring of complicated traffic scene.

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Frequent Items Mining Algorithm Over High Speed Network Flows Based on Double Hash Method

International Journal of Future Generation Communication and Networking ◽

10.14257/ijfgcn.2016.9.5.08 ◽

2016 ◽

Vol 9 (5) ◽

pp. 75-82

Author(s):

Lei Bai ◽

Chao Chen

Keyword(s):

High Speed ◽

Network Flows ◽

Mining Algorithm ◽

High Speed Network ◽

Frequent Items

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Rough set based ensemble learning algorithm for agricultural data classification

Filomat ◽

10.2298/fil1805917s ◽

2018 ◽

Vol 32 (5) ◽

pp. 1917-1930 ◽

Cited By ~ 1

Author(s):

Lei Shi ◽

Qiguo Duan ◽

Juanjuan Zhang ◽

Lei Xi ◽

Hongbo Qiao ◽

...

Keyword(s):

Ensemble Learning ◽

Rough Set ◽

Rough Set Theory ◽

Learning Algorithm ◽

Uncertain Data ◽

Data Classification ◽

Research Area ◽

Experimental Comparison ◽

Classification Problems ◽

Ensemble Learning Algorithm

Agricultural data classification attracts more and more attention in the research area of intelligent agriculture. As a kind of important machine learning methods, ensemble learning uses multiple base classifiers to deal with classification problems. The rough set theory is a powerful mathematical approach to process unclear and uncertain data. In this paper, a rough set based ensemble learning algorithm is proposed to classify the agricultural data effectively and efficiently. An experimental comparison of different algorithms is conducted on four agricultural datasets. The results of experiment indicate that the proposed algorithm improves performance obviously.

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An Improved Algorithm of Improved Computation Efficiency on LTS Hausdorff Distance

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.347-350.3217 ◽

2013 ◽

Vol 347-350 ◽

pp. 3217-3221

Author(s):

Hui Wang ◽

Guo Jia Li ◽

Jun Hui Pan ◽

Fu Hua Shang

Keyword(s):

Hausdorff Distance ◽

Curve Segment ◽

Original Algorithm ◽

Search Path ◽

Computation Efficiency ◽

Traditional Algorithm ◽

Better Than ◽

Improved Algorithm

The computation efficiency of traditional algorithm is not high, and there is more time consuming. This paper presents an effective method for improved hausdorff distance, depth correction of logging curves is based on improved Hausdorff distance. In this method. On the basis of existing LTS hausdorff distance, the contrast curve segment is divided into neighborhood in an area, the LTS hausdorff distance is calculated by using engineering approximate, and the improving methods of search path is put forward, which ensures that the improved algorithm is better than the original algorithm has high computing efficiency and accuracy in theory.

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