Chapter 21 Web Mining — Concepts, Applications, and Research Directions Jaideep Srivastava, Prasanna Desikan, Vipin Kumar Web mining is the application of data. Purpose and Use. The RBDMS Data Mining application integrates RBDMS data, GIS, and full-text searching in a public-facing Web. This integration has been found to be. Data Mining Applications & Trends Cluster Analysis, Mining Text Data, Mining World Wide Web, Applications, Application Exploration.
Data Mining in Web Applications Computer and Information Science» Artificial Intelligence» "Data Mining and Knowledge Discovery in Real Life Applications. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the. Chapter 21 Web Mining — Concepts, Applications, and Research Directions Jaideep Srivastava, Prasanna Desikan, Vipin Kumar Web mining is the application of data. Purpose and Use. The RBDMS Data Mining application integrates RBDMS data, GIS, and full-text searching in a public-facing Web. This integration has been found to be. Data Mining Applications & Trends Cluster Analysis, Mining Text Data, Mining World Wide Web, Applications, Application Exploration.
Data Mining - Applications & Trends
Data mining is widely used in diverse areas. There are a number of commercial data mining system available today and yet there are many challenges in this field. In this tutorial, we will discuss the applications and the trend of data mining.
Data Mining Applications
Here is the list of areas where data mining is widely used −
- Financial Data Analysis
- Retail Industry
- Telecommunication Industry
- Biological Data Analysis
- Other Scientific Applications
- Intrusion Detection
Financial Data Analysis
The financial data in banking and financial industry is generally reliable and of high quality which facilitates systematic data analysis and data mining. Some of the typical cases are as follows −
Design and construction of data warehouses for multidimensional data analysis and data mining.
Loan payment prediction and customer credit policy analysis.
Classification and clustering of customers for targeted marketing.
Detection of money laundering and other financial crimes.
Data Mining has its great application in Retail Industry because it collects large amount of data from on sales, customer purchasing history, goods transportation, consumption and services. It is natural that the quantity of data collected will continue to expand rapidly because of the increasing ease, availability and popularity of the web.
Data mining in retail industry helps in identifying customer buying data mining web application and trends that lead to improved quality of customer service and data mining web application customer retention and satisfaction, data mining web application. Here is the list of examples of data mining in the retail industry −
Design and Construction of data warehouses based on the benefits of data mining.
Multidimensional analysis of sales, customers, products, time and region.
Analysis of effectiveness of sales campaigns.
Product recommendation and cross-referencing of items.
Today the telecommunication industry is one of the most emerging industries providing various services such as fax, pager, cellular phone, internet messenger, images, e-mail, web data transmission, etc. Due to the development of new computer and communication technologies, the telecommunication industry is rapidly expanding. This is the reason why data mining is become very important to help and understand the business.
Data mining in telecommunication industry helps in identifying the telecommunication patterns, catch fraudulent activities, make better use of resource, and improve quality of service, data mining web application. Here is the list of examples for which data mining improves telecommunication services −
Multidimensional Analysis of Telecommunication data.
Fraudulent pattern analysis.
Identification of unusual patterns.
Multidimensional association and sequential patterns analysis.
Mobile Telecommunication services.
Use of visualization tools in telecommunication data analysis.
Biological Data Analysis
In recent times, we have seen a tremendous growth in the field of biology such as genomics, proteomics, functional Genomics and biomedical research. Biological data mining is a very important part of Bioinformatics. Following are the aspects in which data mining contributes for biological data analysis −
Semantic integration of heterogeneous, distributed genomic and proteomic databases.
Alignment, indexing, similarity search and comparative analysis multiple nucleotide sequences.
Discovery of structural patterns and analysis of genetic networks and protein pathways.
Association and path analysis.
Visualization tools in genetic data analysis.
Other Scientific Applications
The applications discussed above tend to handle relatively small and homogeneous data sets for which the statistical techniques are appropriate. Huge amount of data have been collected from scientific domains such as geosciences, astronomy, etc. A large amount of data sets is being generated because of the fast numerical simulations in various fields such as climate and ecosystem modeling, chemical engineering, fluid dynamics, etc. Following are the applications of data mining in the field of Scientific Applications −
- Data Warehouses and data preprocessing.
- Graph-based mining.
- Visualization and domain specific knowledge.
Intrusion refers to any kind of action that threatens integrity, confidentiality, or the availability of network resources. In this world of connectivity, security has become the major issue. With increased usage of internet and availability of the tools and tricks for intruding amd nvidia mining card attacking network prompted intrusion detection to become a critical component of network administration. Here is the list of areas in which data mining technology may be applied for intrusion detection −
Development of data mining algorithm for intrusion detection.
Association and correlation analysis, aggregation to help select and build discriminating attributes.
Analysis of Stream data.
Distributed data mining.
Visualization and query tools.
Data Mining System Products
There are many data mining system products and domain specific data mining applications. The new data mining systems and applications are being added to the previous systems. Also, efforts are being made to standardize data mining languages.
Choosing a Data Mining System
The selection of a data mining system depends on the following features −
Data Types − The data mining system may handle formatted text, record-based data, and relational data. The data could also be in ASCII text, relational database data or data warehouse data. Therefore, we should check what exact format the data mining ninja mining games can handle.
System Issues − We must consider the compatibility of a data mining system with different operating systems. One data mining system may run on only one operating system or on several. There are also data mining systems that provide web-based user interfaces and allow XML data as input.
Data Sources − Data sources refer to the data formats in which data mining system will operate. Some data mining system may work only on ASCII text files while others on multiple relational sources. Data mining system should also support ODBC connections or OLE DB for ODBC connections.
Data Mining functions and methodologies − There are some data mining systems that provide only one data mining function such as classification while some provides multiple data mining functions such as concept description, discovery-driven OLAP analysis, association mining, linkage analysis, statistical analysis, classification, prediction, data mining web application, clustering, data mining web application analysis, similarity search, data mining web application data mining with databases or data warehouse systems − Data mining systems need to be coupled with a database or a data warehouse system. The coupled components are integrated into a uniform information processing environment, data mining web application. Here are the types of coupling listed below −
- No coupling
- Loose Coupling
- Semi tight Coupling
- Tight Coupling
Scalability − There are two scalability issues in data mining −
Row (Database size) Scalability − A data mining system is considered as row scalable when the number or rows are enlarged 10 times. Pre feasibility mining takes no more than 10 times to execute a query.
Column (Dimension) Salability − A data mining system is considered as column scalable if the mining query execution time increases linearly with the number of columns.
Visualization Tools − Visualization in data mining can be categorized as follows −
- Data Visualization
- Mining Results Visualization
- Mining process visualization
- Visual data mining
Data Mining query language and graphical user interface − An easy-to-use graphical user interface is important to promote user-guided, interactive data mining. Unlike relational database systems, data mining systems do not share underlying data mining query language.
Trends in Data Mining
Data mining concepts are still evolving and here are the latest trends that we get to see in this field −
- Application Exploration.
- Scalable and interactive data mining methods.
- Integration of data mining with database systems, data warehouse systems and web database systems.
- SStandardization of data mining query language.
- Visual data mining.
- New methods for mining complex types of data.
- Biological data mining.
- Data mining and software engineering.
- Web mining.
- Distributed data mining.
- Real time data mining.
- Multi database data mining.
- Privacy protection and information security in data mining.
Data Mining — RBDMSData Mining Applications & Trends Cluster Analysis, Mining Text Data, Mining World Wide Web, Applications, Application Exploration. Integrating Predictions with Web Applications- Free online tutorials for Data Mining (2234) courses with reference manuals and examples. Mar 06, 2009 · Hi I already deployed and processed data mining models that I am using for selling predictions. I am working with associaton rules and time series. Top Free Data Mining Software: and application development. Data Mining,Time Series, Image Processing, Web Analytics. Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications) [Bing Liu] on Amazon.com. 4.8/5(10).
Integrating Predictions with Web Applications- Free online tutorials for Data Mining (2234) courses with reference manuals and examples. Purpose and Use. The RBDMS Data Mining application integrates RBDMS data, GIS, and full-text searching in a public-facing Web. This integration has been found to be. Data Mining in Web Applications Computer and Information Science» Artificial Intelligence» "Data Mining and Knowledge Discovery in Real Life Applications.
How to build a data mining web app?
You will need some basic programming and statistical skills. Web Development, jQuery, Python, and Machine Learning skills are a plus. If you can look at new data and immediately see where data mining adds new value, then you are definitely overqualified to use this source code.
The first step is to get your own data. Is there any websites that you visit every day? I'm sure they produce fresh content every day: new articles, new stats, new numbers. How about you start collecting them? If you have your own data you decide what interesting question it may answer.
Next step. Does your favorite website have any trends? More articles are published in summer than in winter? More people are willing to "like" articles in spring than in autumn. Is it possible to predict which article will create more web traffic, thus, more revenue from advertisements?
Finally, once you mine the answer you should display it. Do it so that it's pleasurable for the eye. Colorful time series or multidimensional scaling should do the trick. Describe your graph so the people not familiar with your project can understand it and enjoy it.
Does it seem like a lot of work? Well, here is a source code that deploys your app with one command on Google App Engine. You just need to focus on where to get the data (ETL), what to do with it (DM), and how to display it (VISUALIZATION). The source code has example that you can swap with an idea of your own.
Little by little you will master how to add monetary value to your data, sell it, or build a business model.
Create an account and new app placeholder at:
Please install Google App Engine SDK from:
Getting started with Python web app development is here:
For lunching the app you can use this nice GUI:
It's also good to have Google Analytics account:
You can also check webmaster tools to make sure your website is properly indexed by Google:
Don't forget to rename your app in the app.yaml file:
Deploy your frontend with one command:
Deploy backend with one command:
Current source code requires at least six data points. That means you have to run "/etl_process" webpage at least six times and then "/dm_process" at least once before you see a graph.
This is example of a simple data mining application. Here Hacker News aggregator is our source of data. The data mining objective is to figure out when is good time to post an article or a story on Hacker News website so other people will up-vote it and it will get to from the "newest" page to "news" page.
This app can serve as a very simple business model where you claim is that your DATA MINING application brings better EXPERIENCE, OUTCOME, and VALUE to existing products. How come? If you start adding new knowledge to existing data you will see the pattern: large data can be abstracted to a small chunk of information that is more valuable than the large dataset. That's how you sell your service. Example? Every day you observe cars; that's a lot of data, however, you notice that around 8 am there are many more cars than at other hours; this is your small chunk of information. This small chunk will save you 30 min of stuck in traffic: better experience, outcome and value.
Most data mining application will have very similar information flow:
That's why the code is organized into three sections:
You can think of the code as a "Hello, World!" web data mining example. You shouldn't be surprised that most of the code went into visualization. That's how you get your customers to buy-in. Data for visualization is obtained using JSON serialization.
More on data sets (ETL) with user recommendations/ratings is here https://gist.github.com/1653794
More on data mining (DM) algorithms is here http://mloss.org/software/
More on data visualization using JS is here https://gist.github.com/1515418.
This app shows some raw data. For more complicated projects it might not be good idea to show the raw data. Too much data on the user interface will clog the decision making process.
The hope is that early stage start-ups can use this code to quickly organize their thoughts and prototype their idea. Google App Engine can run this app for free, giving opportunity to demonstrate a working version of their idea.
If you are ambitious and you want to add value to your data using sophisticated statistical methods I suggest you watch Stanford on-line classes at http://www.ml-class.org (if you don't want to login to get the video lectures, here is the hack: http://news.ycombinator.com/item?id=3335753)
Similar data mining app:
Remember, time series analysis is just a small portion of data mining.
Here is example of a business model that does exactly that:
You will notice that the website has these three components:
Another example of a working buisness model is SHOPOBOT:
They data mine prices on Amazon marketplace.
The hardest thing is finding a value in data. One solution would be applying "time is money" and "money is value" rule to your data. Often times search does exactly that. Both SEATGEEK and SHOPOBOT search for good shopping deals and save you time and money. The same thinking might be applied to many other areas of daily activities beside electronics and tickets. This is good start. But as soon as you master finding places where your web app saves time and money you should move to more advanced areas where domain expertise is needed. Medicine, finance, litigation, and manufacturing are four big markets. Each is producing tons of data every day. Find an expert, a medical doctor or an accountant, ask about a mundane task that one performs every day but could be automated. You just earned your first $1 mln!
There are books that cover the topic of finding business value in data, just one of them:
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