Prediction — This data mining technique identifies the relationship between independent and dependent variables and is mainly used in predicting the future for a sale. It is an important technique of data mining in which repetitive pattern is recognized in intelligent environments. It helps in predicting future events.
Sequential Analysis — Sequential analysis is a technique that discovers and identifies similar patterns, events, and trends in transactional data over a certain period of time. There are various real-life examples of data mining from everyday life. The most common example for this is cross-selling by e-commerce sites based on the searches made by the customer on the web.
Another example for this is the loyalty card programme run by various stores and markets to gather valuable customer information. Fraud detection, particularly in the field of telecommunication and card sale service, is another example for this. Data mining helps in determining duration, location and time of the call in case of fraud calls.
Data mining is used in wide range of areas from telecommunication to financial areas. It is also being taught as a subject in various colleges as a part of the curriculum, particularly in computer science. For masters students, this is a very good thesis topic as well as for research. Numerous agencies are available over the Internet that will provide thesis writing assistance and help for data mining.
It is a relatively new technology and yet to reach a wider audience. A lot of data is generated in medical science every day which needs to be managed. Data Mining is useful in this case for extracting valuable information from this data thus generated. Data Mining is helpful in medical science to:. Data Mining can be used to analyze customer behavior by tracking his different purchases and daily activities. We can get information about how much does a customer spends using his credit card and which product he usually buys.
Data Mining is very helpful, particularly in marketing and sales business. Through data mining, marketing and sales enterprises can make offers to customers based on their purchases and also on what product he usually searches. Data Mining also finds its application in the field of science and engineering for the development of new products like sensor devices and pattern recognition system.
Data Mining also finds its application in Machine Learning, pattern recognition, database management and artificial intelligence. Web Mining is an application of Data Mining and an important topic for research and thesis. It is a technique to discover patterns from WWW i. The information for web mining is collected through browser activities, page content and server logins. It is a very good area for master thesis data mining.
There are three types of Web Mining:. It is a technique to extract usage patterns from Web Data. These patterns are used for understanding the needs of Web-based applications. Web usage mining can also be classified according to the following type of data:. Web Content Mining refers to the extraction of useful information and data from Web Page content. For retrieving information from the web page intelligent tools like web agents are used.
Intelligent Systems are created which involve this agent-based approach. In this technique, graph theory is used for analyzing the node and structure of the website. It can be classified into two different types :. It is an important field of Data Mining. It refers to the process of extracting valuable information from text and is also referred to as text analytics. This high-quality information is extracted through patterns and methods like statistical pattern learning.
It is another good area for the Ph. In Text Mining, input data is structured and patterns are derived from this structured data. There are various research areas and thesis topics in the field of text mining. For any thesis help on data mining, contact us.
Techsparks provides thesis guidance in data mining. Tags: data mining , data mining process and techniques , data set , education , M. Search for:. Reviews Contact Us Get Directions. Mining ca n be considered a pplie d on the internet documents once we ll the res ults pa ges produce of a internet search engine.
The re are two kinds of a pproach in content mining ca lle d age nt base d a p- proa ch a nd da ta base base d a pproa ch. Age nt base d a pproach conce ntra te on ocean rching re leva nt informa tion us ing the cha racte- ris tics of the particular doma directly into interpre t a nd organize the co l- lecte d informa tion.
The information base a pproach is use d for re trie ving the se mi-s tructure da ta on the internet. Web mining may be the term of applying data mining strategies to instantly uncover and extract helpful information from the internet documents and services. The unstructured feature of Web data triggers more complexity of Web mining. Web mining scientific studies are really a converging area from the 3 research communities, for example Database, Information Retrieval, Artificial Intelligence, as well as psychology and statistics too.
Web mining requires the analysis of Server logs of an internet site. The Net server logs retain the entire assortment of demands produced by a possible or current customer through their browser and responses through the Server.
The data within the logs varies with respect to the log extendable and option selected on the internet server. Research into the Web logs could be insightful for handling the corporate e- business on the short-term basis the actual worth of this understanding is acquired through integration of the resource along with other customer touch point information. Common applications include Site usability, road to purchase, dynamic content marketing, user profiling through behavior analysis and product affinities.
Web mining describes the use of traditional data mining techniques to the web sources and it has facilitated the further growth and development of they to think about the particular structures of web data. The examined web sources contain 1 the particular site 2 backlinks connecting these websites and 3 the road that internet surfers take on the internet to achieve a specific site. Web usage mining then refers back to the derivation of helpful understanding from all of these data inputs.
Web mining finds out patterns inside a less structured data for example Internet. Quite simply, we are able to state that Web Mining is Data Mining techniques put on the. However , various kinds of users have different preferences, background, understanding etc. Web Usage Mining is the use of data mining strategies to uncover interesting usage patterns from Web data, to be able to understand and serve the requirements of Web-based applications.
It attempts to discovery the helpful information in the secondary data produced from the interactions from the users while surfing on the internet. Usage data captures the identity or origin of Internet users with their browsing behavior at an internet site. Because the content and structure mining make use of the real or primary data on the internet.
|Web content mining thesis||The World Wide Web store, share and distribute information in the large scale. PDF Version View. Web was available. Now much of the information is resume telephone sales available on the Web. Web Structure Mining Web Structure Mining tries to discover useful knowledge from the structure and hyperlinks. Now, we should compute the so-called class conditional probabilities of thefeatures given the available classes.|
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|Web content mining thesis||This thesis implements, materializes and extends the structured automatic data extraction technique. Recently, a number of researchers also make use of common language patterns common sentence structures used to express certain facts or relations and redundancy of information on the Web to find concepts, relations among concepts and named entities. Instead, it exploits existing organizational structures in the original Web documents, emphasizing tags and language patterns to perform data mining to find important concepts, sub- concepts and their hierarchical relationships. The tree tries to infer a split of the training data basedon the values of the available features to produce a good generalization. With an appropriate nonlinear mapping to web content mining thesis sufficientlyhigh dimension, data from two classes can always beseparated by a hyperplane. Wandra K.|