Data Mining :
Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information – information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Data mining is primarily used today by companies with a strong consumer focus – retail, financial, communication, and marketing organizations. It enables these companies to determine relationships among “internal” factors such as price, product positioning, or staff skills, and “external” factors such as economic indicators, competition, and customer demographics. And, it enables them to determine the impact on sales, customer satisfaction, and corporate profits. Finally, it enables them to “drill down” into summary information to view detail transactional data.
Functions of Data Mining :
1. Class Description
6. Time-series analysis
Association Analysis :
The purpose of association analysis is to find patterns in particular in business processes and to formulate suitable rules, of the sort “If a customer buys product A, that customer also buys products B and C”.
Tip : If a customer buys mozzarella at the supermarket, that customer also buys tomatoes and basil.
Association analysis also helps you to identify cross-selling opportunities, for example. You can use the rules resulting from the analysis to place associated products together in a catalog, in the supermarket, or in the Web shop, or apply them when targeting a marketing campaign for product C at customers who have already purchased product A.
Association analysis determines these rules by using historic data to train the model. You can display and export the determined association rules.
The different types of disasters can be classified into two categories. They are:
A natural disaster is a major adverse event resulting from the earth’s
natural hazards. Examples of natural disasters are floods,
tsunamis, tornadoes, hurricanes/cyclones, volcanic
eruptions, earthquakes, heat waves, and landslides.
Man-made disasters are the consequence of technological or human
hazards. Examples include stampedes, urban fires, industrial
accidents, oil spills, nuclear explosions/nuclear radiation and acts
Develop disaster recovery plan
Step1: Risk Analysis
The first step in drafting a disaster recovery plan is conducting a
thorough risk analysis of our computer systems. List all the possible
risks that threaten system uptime and evaluate how imminent they are in our particular IT shop.
Step 2: Establish the Budget
Once you’ve figured out your risks, ask ‘what can we do to suppress them, and how much will it cost?’
Step 3: Develop the Plan
The feedback from the business units will begin to shape your DRP procedures. The recovery procedure should be written in a detailed plan or “script.” Establish a Recovery Team from among the IT staff and assign specific recovery duties to each member and Define how to deal with the loss of various aspects of the network and specify who arranges for repairs or reconstruction and how the data recovery process occurs.
Step 4: Test
Once your DRP is set, test it frequently. Eventually you’ll need to
perform a component-level restoration of your largest databases to get a realistic assessment of your recovery procedure, but a periodic
walk-through of the procedure with the Recovery Team will assure
that everyone knows their roles.