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Data Mining Overview |
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We deliver the benefits of data mining technology to our customers by developing
capabilities to manage and extract information from structured and unstructured
data. Using data mining tools, we help organizations sift through the large databases
and identify previously hidden patterns and present the information to meet the
requirements of wide spectrum of users across the organization. We follow standard
data mining techniques and methodologies to collate and present intelligent information
needed for decision makers to improve business processes, make better decisions
and enhance customer satisfaction. |
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Data Mining allows you to analyze data and issue predictions, uncovering opportunities.
The data that your company generates during the course of normal operation can be
automatically processed to give important insight into the state of business. Our
custom Data Mining solutions will allow you to summarize data, indicate anomalies
within the data, reveal necessary and sufficient conditions, point out cases deviating
from or satisfying certain rules and get a more accurate picture of the future. |
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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. |
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Data mining is frequently described as "the process of extracting valid, authentic,
and actionable information from large databases." In other words, data mining derives
patterns and trends that exist in data. These patterns and trends can be collected
together and defined as a mining model. |
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Data mining commonly involves four classes of task: |
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Classification - Arranges the data into predefined groups. For example an email
program might attempt to classify an email as legitimate or spam. Common algorithms
include Nearest neighbor, Naive Bayes classifier and Neural network. |
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Clustering - Is like classification but the groups are not predefined, so the algorithm
will try to group similar items together.
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Regression - Attempts to find a function which models the data with the least error.
A common method is to use Genetic Programming.
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Association rule learning - Searches for relationships between variables. For example
a supermarket might gather data of what each customer buys. Using association rule
learning, the supermarket can work out what products are frequently bought together,
which is useful for marketing purposes. This is sometimes referred to as "market
basket analysis".
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Benefits of Data Mining |
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- Analysis and reporting as required for business needs
- Integration for online reporting on internet / Intranet
- Regulatory reports with any frequency with any data
- Multiple channels like paper, print, fax, mobile, online channels, live feeds etc with the data
- Reduction in manual work and quality of outputs much faster and easier
- Flexibility in MIS and reporting depending on needs
- Data security and controlled circulation with minimal data movement
- Data availability for research, analysis, forecasts, trends, predictions and better utilization of resources
- Training with the data and better usage of results in a timely and accurate fashion
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Four Phases of Data Mining |
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Data Preparation |
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- Identify the main data sets to be used by the data mining operation (usually the
data warehouse) |
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Data Analysis and Classification |
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- Study the data to identify common data characteristics or patterns |
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- Data groupings, classifications, clusters, sequences
- Data dependencies, links, or relationships
- Data patterns, trends, deviation
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Knowledge Acquisition |
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- Uses the Results of the Data Analysis and Classification phase.
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- Data mining tool selects the appropriate modeling or knowledge-acquisition algorithms
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- Neural Networks
- Decision Trees
- Rules Induction
- Genetic algorithms
- Memory-Based Reasoning
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Prognosis
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- Predict Future Behavior
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- Forecast Business Outcomes |
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- 65% of customers who did not use a particular credit card in the last 6 months are 88% likely to cancel the account
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Extraction of Knowledge from Data |
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Data |
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Data are any facts, numbers, or text that can be processed by a computer. Today,
organizations are accumulating vast and growing amounts of data in different formats
and different databases. This includes:
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- Operational or transactional data such as, sales, cost, inventory, payroll, and accounting
- Nonoperational data, such as industry sales, forecast data, and macro economic data
- Meta data - data about the data itself, such as logical database design or data dictionary definitions
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Information
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The patterns, associations, or relationships among all this data can provide information.
For example, analysis of retail point of sale transaction data can yield information
on which products are selling and when.
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Knowledge
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Information can be converted into knowledge about historical patterns and future
trends. For example, summary information on retail supermarket sales can be analyzed
in light of promotional efforts to provide knowledge of consumer buying behavior.
Thus, a manufacturer or retailer could determine which items are most susceptible
to promotional efforts.
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