The concepts and capabilities of data mining

The concepts and capabilities of data mining

During my studies I also faced that types of problems but I studied different types of books and got their answers. Ok now the answer of your question is that. Data mining is used to examine or explore the data using queries. These queries can be fired on the data warehouse.

Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc. It is more commonly used to transform a large amount of data into a meaningful form. Data here can be facts, numbers or any real time information like sales figures, cost, Meta data etc. Information would be the patterns and the relationships amongst the data that can provide information.

The concepts and capabilities of data mining

Data mining commonly involves four classes of tasks:

• Association rule learning – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
• Clustering – Is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
• Classification – Is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam. Common algorithms include decision tree learning, nearest neighbor, naive Bayesian classification, neural networks and support vector machines.
• Regression – Attempts to find a function which models the data with the least error.

Another example of data mining, often called the market basket analysis, relates to its use in retail sales. If a clothing store records the purchases of customers, a data-mining system could identify those customers who favor silk shirts over cotton ones.

Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical or inexact rules may also be present within a database.

Market basket analysis has also been used to identify the purchase patterns of the Alpha consumer. Alpha Consumers are people that play a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society.

Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.

https://en.wikipedia.org/wiki/Data_mining

dataminingtools. net/wiki/intodm.php

The concepts and capabilities of data mining

Data mining is the process of discovering patterns in large sets of data. Extracting data and information from large sets of data is an important topic in machine learning and database systems, and it is highly recognized as a crucial area that holds the opportunity of major revenues.

The concepts and capabilities of data mining

DATA MINING:

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses.

Concepts and Capabilities of data mining:

Data mining is the process of extracting patterns from huge amount of data. It is the core knowledge of the discovery process. Relational, data warehouse, transactional, stream, object oriented, spatial, text, multi-media, heterogeneous, legacy and WWW data is mined. The techniques utilized for data mining are database-oriented, data warehousing (OLAP), visualization, machine learning etc.

Data mining can be implemented in industries like, retail, stock market, text mining, web mining, banking, telecommunication to name a few. Data mining functionalities are characterization, discrimination, clustering, outlier and trend analysis, classification, etc.

The concepts and capabilities of data mining

The concept of DATA MINING is to collect specific data and to create possible solutions and possible predictions for a specific theory. In this manner, DATA MINING has 3 stages for easy and thorough collection of data, interpreting it, and finally applying those collected data to arrive for an answer to a number of problems.

These 3 stages, namely, EXPLORATION also known as DATA EXPLORATION, PATTERN IDENTIFICATION or DATA RECOGNITION, and finally DEPLOYMENT or APPLICATION should be followed step-by-step in order to accumulate a desirable outcome.

In EXPLORATION stage, as said on the name, you must explore through a set of data and collect important details which may be subjected to further analysis. In this stage, there are tools of which can you used to furthermore simplify the presentation of data. You can use charts and graphs to present the collected data.

The second stage is PATTERN IDENTIFICATION in which you will now compare your collected data from other data which might be more specific and look for a specific pattern that will allow you to make the best predictions. In your predictions, you must select the one that functions to its highest level.

The third stage is DEPLOYMENT. This stage comes after you have discovered a pattern that is consistent enough and highly predictive in the second stage. You can apply your pattern directly to have a desirable result. In other words, you can select the most favorable pattern that was discovered in stage 2 and applying that pattern to attain a good output.

DATA MINING is widely used to improve the quality of GAMES, BUSINESS, and in SCIENCE AND ENGINEERING. It does not only generate more profit but also it helps those sectors to establish firmly and evolve from time to time.

The capabilities of DATA MINING are endless when being applied to a certain field. Let us take the BUSINESS sector as an example. A market-basket analysis could identify the number of customers who are likely to buy silk-made garments over cottons. This could be taken advantage by predicting that more customers for a specific period will do the said characteristic and by taking advantage of it, the store will increase its profit.

The concepts and capabilities of data mining

 Data Mining consists of 5 Major Elements

• Extract, transform and load transactions data onto  the data warehouse system.
• Store and manage data in a multi-dimensional database system.
• Provide data access to business analysts and information technology professionals. Analyze the data by application software.
•  Present the data in a useful format such as graph or table.

While large scale information technology has been evolving separate transaction and analytical systems,

         DATA MINING provides the link between the two. 

         Data Mining software analyzes relationships and patterns in stored transactions database on open-ended user queries.

Voluntary Churn Model

Example of Data Mining structure

In this example the goal of a mobile telephony network operator is to set up a model for the early identification of potential voluntary churners. This model will be the base for a respective targeted retention campaign and predicts voluntary attrition 3 months ahead.

The following illustration presents its set-up.

The concepts and capabilities of data mining

Data mining is very important, even though it requires a lot of time and might seem boring and monotonous.

If you don’t extract data, you might miss out on very important information hidden in your site statistics.