Data Mining - Concepts and Techniques.

In data mining technique Decision Tree is an important method of Classification, it is used for prediction and forecasting from historical data. In this survey paper we are surveying effective methods for detection of web based attacks using various data mining techniques such as classification, clustering, partitioning. With the advent of network and the e-commerce technologies we are depends on e-transactions, so the internet works using browsers, attacker has to attacks on the websites and the web servers, at the web server it maintains the log file which consists of web application queries, for applying data mining algorithms we requires only that the web application queries be slightly pre-processed before application. In this survey we are presenting review of various data mining techniques useful for detection of web based attacks.

A Review on Detection of Web Based Attacks using Data Mining Techniques

The ability of managers to analyze large volumes of data is not enough to identify all relevant associations and necessary for the decision-making process. Make use of a classification model and clustering model can generate information that typically a manager could not create without the utilization of this technology. The aim of this work is to reach a classification model linking them to clusters, based on data from purchases made by customers through electronic media in an automated manner. Precisely this model presents a set of rules to assist in decision-making applicable to a sale of vehicles, parts and accessories. For the construction of this model, we applied a process of knowledge discovery in databases. In which classification techniques and clustering techniques was evaluated in an experiment regarding accuracy, interpretability and learned a model of the computing performance. Data mining has been used to find this classifier.

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Knowledge Discovery Using an Integration of Clustering and Classification to Support Decision-making in E-commerce

The ability of managers to analyze large volumes of data is not enough to identify all relevant associations and necessary for the decision-making process. Make use of a classification model can generate information that typically a manager could not create without the utilization of this technology. The aim of this work is to reach a classification model, based on data from purchases made by customers through electronic media in an automated manner. Precisely this model presents a set of rules to assist in decision-making applicable to a sale of vehicles, parts and accessories. For the construction of this model, we applied a process of knowledge discovery in databases, in which classification techniques was evaluated in an experiment regarding accuracy, interpretability and learned model to the computing performance. Data mining has been used to find this classifier.

An Approach Based on Data Mining to Support Management in E-Commerce

There is a wide-ranging use of Peer-to-Peer (P2P) computing and applications in majority of the key areas of Engineering and Technology. Devoid of any centralized server, they can share their content since peers are linked with each other. This is the reason why P2P computing gives enhanced communication among peers. It is essential for the video server to maintain the data content link in cache memory so the cache memory sizes will be enlarged to a definite level and also the cache needs to be securely sustained by each and every peers. By utilizing the Machine Learning method, the proposed method centers its concentration on classifying the video server depending on seasonal and non seasonal popularity. Two supervised Machine Learning algorithms are utilized in this paper and are explained as follows. The Case-Based Reasoning algorithm is utilized in order to sort out well-liked videos and the Averaged One-Dependence Estimators (AODE) algorithm is utilized to sort out video server into seasonal and non-seasonal. The first algorithm is based on Retrieve, Reuse, Revise and Retain methods and the latter algorithm sorts out the video server into seasonal and non-seasonal based video servers. The work simulated by Java programming language.

http://ieeexplore.ieee.org/iel7/7112413/7124722/07124778.pdf

Categorize the video server in P2P networks based on seasonal and normal popularity videos using machine learning approach

Customer information-mining in e-commerce is very important. ID3 algorithm is a mining one based on decision tree, which selects property value with the highest gains as the test attribute of its sample sets, establishes decision-making node, and divides them in turn. ID3 algorithm involves repeated logarithm operation, and it will affect the efficiency of generating decision tree when there are a large number of data, so one must change the selection criteria of data set attributes, using the Taylor formula to transform the algorithm to reduce the amount of data calculation and the generation time of decision trees and thus improve the efficiency of the decision tree classifier. It is shown that the use of improved ID3 algorithm to deal with the customer base data samples can reduce the computational cost, and improve the efficiency of the decision tree generation.

Study on the application of data mining for customer groups based on the modified ID3 algorithm in the e-commerce

There is a lot of vague information about TCM (Traditional Chinese Medicine) diagnosis difficult to understand through computer. Reasoning process, by coalescing of the CBR (Case-based Reasoning) and FBR (Fuzz-based Reasoning) in artificial intelligence, makes up for shortcomings of their alone, can achieve a good understanding of information in TCM diagnosis. As for the new diagnostic features, the reasoning algorithm firstly finds them in the existing case base, if fails, fuzzy reasoning mechanism will start automatically, then ultimately the credible results will be presented to the user. Facts have shown that the algorithm has good reasoning ability and can get more accurate diagnoses.

Feng Yang

Papers

Study on the application of data mining for customer groups based on the modified ID3 algorithm in the e-commerce

Reasoning research on vague information based on case-based reasoning and fuzzy-based reasoning in traditional Chinese medicine diagnosis