Drug Students’ Career Planning System Based on Artificial Intelligence Technology and Web Data Mining Algorithm

In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

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Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

IEEE Access ◽

10.1109/access.2020.2981632 ◽

2020 ◽

Vol 8 ◽

pp. 53394-53407

Author(s):

Yuan Huang ◽

Zhe Cheng ◽

Qianyu Zhou ◽

Yuxing Xiang ◽

Ruixiao Zhao

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Data Mining Algorithm ◽

Network Information ◽

Decision Mechanism ◽

Mining Algorithm ◽

Cloud Network

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A Web Data Mining Algorithm Based on Weighted Association Rules

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.467-469.1386 ◽

2011 ◽

Vol 467-469 ◽

pp. 1386-1391 ◽

Cited By ~ 1

Author(s):

Xiao Lv ◽

Yong Jie Li ◽

Xu Lu

Keyword(s):

Data Mining ◽

Association Rules ◽

Frequent Itemsets ◽

Data Mining Algorithm ◽

Web Data ◽

Web Data Mining ◽

Association Rules Mining ◽

Weighted Association Rules ◽

New Association ◽

Mining Model

Association rules mining is attracting much attention in research community due to its broad applications. Existing web data mining methods suffer the problems that 1) the large number of candidate itemsets, which are hard to be pruned, should be pruned in advance. 2) the time of scanning the database, which are needed to scan transactional database repeatedly, should be reduced. In this paper, a new association rules mining model is introduced for overcoming above two problems. We develop an efficient algorithm-WARDM(Weighted Association Rules Data Mining) for mining the candidate itemsets. The algorithm discusses the generation of candidate-1 itemset, candidate-2 itemset and candidate-k itemset(k>2),which can avoid missing weighted frequent itemsets. And the transactional database are scanned only once and candidate itemsets are pruned twice, which can reduce the amount of candidate itemsets. Theoretical analysis and experimental results show the space and time complexity is relatively good, Meanwhile the algorithm decreases the number of candidate itemsets, enhances the execution efficiency.

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