Web Mining Taxonomy

what is web usage mining

Web usage mining is a Web Mining paradigm in which Data Mining techniques are applied to Web usage data. As for Data Mining in general, the goal can be to construct a model of users’ behaviour or to directly construct an adaptive system. A potential advantage of an explicit user model is that it can be used for different purposes where an adaptive system has a specific function. For modelling user behaviour, usage mining is combined with other information about users.

Many aspects of users can be modelled:
Their interaction with a system, their interests, their knowledge, their geographical behaviour and also of course combinations of these. Modelling preferences needs information about users preferences for individual objects. This is often problematic because users are not always prepared to evaluate objects and enter the evaluations. Therefore other data are used like downloading, buying or time data.
Adaptive systems have the purpose to improve some aspect of the behaviour of the system. Improvements can be system-oriented, content-oriented (e.g. presenting information or products that relevant for the user), or business-oriented (e.g. presenting advertisements that the user is likely to buy, or that the vendor prefers to sell).

Another dimension is wether the model or adaptation concerns individual users personalisation
or generic system behaviour. An intermediate form is to use usage information obtained during a session to adapt system behaviour.

This can also be combined with personalisation. System-oriented adaptation based on usage mining is aimed at performance optimisation, e.g. for Web servers. This is of paramount importance in large sites that incur a lot of traffic.

One of the factors leading to performance degradation is access to slow peripherals like disks, from which pages are pre-fetched upon user demand. Hence, it is of interest to devise intelligent pre-fetching mechanisms that allow for efficient caching.

The problem specification reduces to a next-event prediction, where the next event is a page fetch request, combined with an appropriate mechanism for refreshing the cash,e.g. least frequently used or most frequently used.

what is Project Managment

Project management is the supervision and control of the work required to complete the project vision. The project team carries out the work needed to complete the project, while the project manager schedules, monitors, and controls the various project tasks. Projects, being the temporary and unique things that they are, require the project manager to be actively involved with the project implementation. They are not self-propelled.

Project management is comprised of the following nine knowledge areas:

■ Project Integration Management This knowledge area focuses on project
plan develop and execution.
■ Project Scope Management This knowledge area deals with the planning,
creation, protection, and fulfillment of the project scope.
■ Project Time Management Time management is crucial to project success.
This knowledge area covers activities, their characteristics, and how they fit
into the project schedule.
■ Project Cost Management Cost is always a constraint in project management.
This knowledge area is concerned with the planning, estimating, budgeting,
and control of costs.
■ Project Quality Management This knowledge area centers on quality
planning, assurance, and control.
■ Project Human Resource Management This knowledge area focuses on
organizational planning, staff acquisition, and team development.
■ Project Communications Management The majority of a project manager’s
time is spent communicating. This knowledge area details how communications
can improve.
■ Project Risk Management Every project has risks. This knowledge area
focuses on risk planning, analysis, monitoring, and control.
■ Project Procurement Management This knowledge area involves planning,
solicitation, contract administration, and contract closeout.

Abstract on Data Mining

It is estimated that the total amount of information in the world doubles every 20 months.This data explosion has meant that the size of databases as well as their numbers have increased dramatically. It is an increasing challenge to make efficient use of this vast amount of information.

As a result of this trend, the field of Knowledge Discovery has been developed to manage and manipulate this data in an efficient manner. Data Mining is a set of techniques that have been developed to extract useful information from this sea of knowledge. Data Mining has wide-ranging applications in a number of fields from the commercial to the academic spheres.


Data mining or knowledge discovery refers to the process of finding interesting information in large repositories of data. The term data mining also refers to the step in the knowledge discovery process in which special algorithms are employed in hopes of identifying interesting patterns in the data. These interesting patterns are then analyzed yielding knowledge. The desired outcome of data mining activities is to discover knowledge that is not explicit in the data, and to put that knowledge to use.

Java ID3 Source Code for Decision Tree Algorithm

Dear All,

The Input in.file is a txt file with a space separating each field (my actual in.file is a web logfile )
so i wish this will be informitive for you all, goodluck.

Six Sigma & Business

I Failed to Implement Six Sigma Techniques, currently i'm losing ma business.

..........OOoohh How Poor you are !!

THE ORIGINS OF SIX SIGMA

Sigma is the letter in the Greek alphabet used to denote standard deviation, a statistical measurement of variation, the exceptions to expected outcomes. Standard deviation can be thought of as a comparison between expected results or outcomes in a group of operations, versus those that fail.

The measurement of standard deviation shows us that rates of defects, or exceptions, are measurable. Six Sigma is the definition of outcomes as close as possible to perfection. With six standard deviations, we arrive at 3.4 defects per million opportunities, or 99.9997 percent. This would mean that at Six Sigma, an airline would lose only three pieces of luggage for every one million that it handles; or that the phone company would have only three unhappy customers out of every one million who use the phone that day. The purpose in evaluating defects is not to eliminate them entirely, but to strive for improvement to the highest possible level that we can achieve.
Key Point We evaluate defects to improve overall performance, knowing that eliminating them completely is unrealistic.