scholarly journals WIM: An Information Mining Model for the Web

2006 ◽  
Author(s):  
R. Baeza-Yates ◽  
A.R. Pereira ◽  
N. Ziviani
Keyword(s):  
Information Mining ◽  
Mining Model ◽  
The Web

An Information Mining Model of Intelligent Collaboration Based on Agent Technology

2014 ◽  
Vol 998-999 ◽  
pp. 1096-1099 ◽  
Author(s):  
Li Yong Wan ◽  
Jian Xin Chen ◽  
Dong Juan Gu
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Agent Technology ◽  
Web Page ◽  
Information Mining ◽  
Characteristic Structure ◽  
Mining Model ◽  
The Web

Information can be collected search engine on the Web, but the current search engines can only interpret the characteristic structure of web page data from the perspective of syntax, is lack of semantic understanding, and thus cannot find the desired information quickly and accurately. In order to solve these problems, this paper proposes an information mining model of intelligent Collaboration based on agent technology. By analyzing the information mining process, more to understand the mechanism of Collaboration mining, thus can help users find faster and better desired information.

Key Data Optimization Mining in Massive Student Database Model

2014 ◽  
Vol 687-691 ◽  
pp. 1466-1469
Author(s):  
Zhen Chao Wang
Keyword(s):  
Data Mining ◽  
Traditional Method ◽  
Experimental Results ◽  
Redundant Information ◽  
Mining Method ◽  
Information Mining ◽  
Database Model ◽  
Data Mining Method ◽  
Mining Model ◽  
Effective Cluster

In the process of massive student data mining using traditional method, special words and related characteristics were used as mining objects. The concealment and feature of deliberately camouflaged of information made it is difficult for mining model to form an effective cluster centers, which reduced the accuracy of information mining. Hence an optimized data mining method was proposed. According to the degree of generalization and fuzziness of the feature words of student, the threshold of mining information was set, which avoided the effects of redundant information, thus the efficiency of mining was improved. The experimental results showed that using the improved algorithm to perform information mining in massive student database could effectively improve mining efficiency.

Text Semantic Mining Model Based on the Algebra of Human Concept Learning

2012 ◽  
pp. 221-236 ◽  
Author(s):  
Jun Zhang ◽  
Xiangfeng Luo ◽  
Xiang He ◽  
Chuanliang Cai
Keyword(s):  
Knowledge Representation ◽  
Concept Learning ◽  
Large Scale ◽  
Construction Process ◽  
Semantic Mining ◽  
Lower Complexity ◽  
Mining Model ◽  
The Given ◽  
The Web ◽  
Better Than

Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model (TSMM) based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.

Text Semantic Mining Model Based on the Algebra of Human Concept Learning

2011 ◽  
Vol 5 (2) ◽  
pp. 80-96
Author(s):  
Jun Zhang ◽  
Xiangfeng Luo ◽  
Xiang He ◽  
Chuanliang Cai
Keyword(s):  
Knowledge Representation ◽  
Concept Learning ◽  
Large Scale ◽  
Construction Process ◽  
Semantic Mining ◽  
Lower Complexity ◽  
Mining Model ◽  
The Given ◽  
The Web ◽  
Better Than

Dealing with the large-scale text knowledge on the Web has become increasingly important with the development of the Web, yet it confronts with several challenges, one of which is to find out as much semantics as possible to represent text knowledge. As the text semantic mining process is also the knowledge representation process of text, this paper proposes a text knowledge representation model called text semantic mining model (TSMM) based on the algebra of human concept learning, which both carries rich semantics and is constructed automatically with a lower complexity. Herein, the algebra of human concept learning is introduced, which enables TSMM containing rich semantics. Then the formalization and the construction process of TSMM are discussed. Moreover, three types of reasoning rules based on TSMM are proposed. Lastly, experiments and the comparison with current text representation models show that the given model performs better than others.

scholarly journals Phishing Attack Detection using Machine Learning

2020 ◽  
Vol 9 (3) ◽  
pp. 483-487
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Database System ◽  
Attack Detection ◽  
Information Mining ◽  
Points Of Interest ◽  
Secure Information ◽  
Number Of Customers ◽  
The Web

There are number of customers who buy items on the web and make installment through different websites. There are various websites who request that client give delicate information, for example, username, secret word or master card points of interest and so on regularly for noxious reasons. This sort of websites is known as phishing site. With a specific end goal to identify and foresee phishing site, we proposed an astute, adaptable and successful framework that depends on utilizing characterization Data mining calculation. We actualized arrangement calculation and strategies to extricate the phishing informational collections criteria to order their authenticity. The phishing site can be identified in light of some imperative attributes.This application can be utilized by numerous E-trade endeavors to influence the entire exchange to process secure. Information mining calculation utilized as a part of this framework gives better execution when contrasted with other conventional orders calculations. With the assistance of this framework client can likewise buy items online with no delay. Administrator can include phishing site url or phony site url into framework where framework could access and sweep the phishing site and by utilizing calculation, it will add new suspicious watchwords to database. System utilizes machinelearning method to include new catchphrases into database.

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