Data Mining Is A New Component In An Enterprise’s Decision Support System

Traditionally, organizations use data tactically – to manage. For the competitive, strong organizations use data strategically – to expand operations to improve profitability, reduce costs, and market more effectively. Data mining (DM) makes information assets that an organization can use these strategic objectives.

In this article we have some important questions to officials on data mining deal. These include:
What is data mining?
It can for my organization?

Business Definition of Data Mining
Data mining is a business decision support systems is new component architecture. Query and reporting as it complements and interlocks with other DSS capabilities, on-line analytical processing, data visualization, and traditional statistical analysis. These other DSS technologies are usually retrospective. He reports, tables, and provide graphically what happened in the do we have ten percent increase in sales to reach your goals? ”

We define data mining as “hidden patterns in data-driven discovery and modeling of large amounts of data.” Data mining is different from the retrospective technologies, because this model produces models that capture and represent patterns hidden in data. There is also a user automatically to discover patterns and the model is in fact without knowing what he was looking for, you can build. Models are both descriptive as possible.

They know why things happen and probably will happen. A user can create “what – if” that can be requested directly from the database or warehouse data mining model to the questions. Examples “Which customers are likely to open a money market account, ‘or’ the customer to cancel our service fee if we introduce the” expected lifetime value of each customer account, “What?”

DM associated with information technology, neural networks, genetic algorithms, fuzzy logic, and rules induction. It is beyond the scope of this article to elaborate on these technologies. Instead, we focus on business needs and how these needs can translate into dollars for the data mining solutions.

Mapping solutions for business and profit
What data mining can do for your organization? Expansion of the company, profitability, cost, and sales and marketing: introduction, we use the data for an organization to describe the strategic opportunities. These possibilities are many cases where companies have successfully implemented through the DM to consider very specific. Let’s take one example in Social Networking.

Twitter applications such as inside a lot of people “tweets”, as stated, Linkedin is unstructured. “Tweets” updated in such applications are similar to our own thought processes. Data mining techniques in data mining is properly defined. For example, a product which color you like most in the survey included such questions? Feature that most do not like you, so on and so forth.

By writing a standard OLAP processing logic of critical business intelligence in order to obtain the required reports to offer. In this case, there is also data definition, data entry and data analysis is a considerable amount of effort spent. Tweets many unstructured information, Facebook etc. To demonstrate the challenges of a mining system that will build on both the probability and statistics will be based.

Joseph Hayden writes article on Data Extraction Services, Web Data Extraction, Website Data Extraction, Web Screen Scraping, Web Data Mining, Web Data Extraction etc..

Data Mining Is Used To Analyzing The Collections Of Observations

Data mining is the process of applying these methods to data with the intention of discovering hidden patterns. It has been used for many years by companies, governments and scientists to sift through volumes of data, including data on air passengers traveling to census data and supermarket scanner data to produce market research reports.

An important reason for using data mining is to assist in analyzing the collections of observations of behavior. This data is vulnerable because of collinearity relationships unknown. A fact of data mining is that all of the data being analyzed may not be representative of the whole field, and therefore can not provide examples of critical behaviors and relationships that exist in other parts of the field.

To overcome this problem, the analysis can be increased by using approaches based on experiments and others, such as the choice models for human generated data. In these situations, inherent correlations either verified or removed during construction of the experimental setup.

Data mining usually involves four categories of tasks:

Layout – Organizes data into predefined groups. For example an email program can try to e-mail as legitimate or spam classification. Common learning algorithms are decision trees, nearest neighbor, naive Bayes classification and neural network.

Clustering – such as format, but the groups are not predefined, so the algorithm will try to group similar items.

Regression – Attempts to find a function that models the data with the least error.

Association rule learning – Searches for relationships between variables. Example, a supermarket might gather data about customers’ buying behavior. Using the learning of association rules, the supermarket determine which products are often purchased together and use this information for marketing purposes. This is called market basket analysis. Now. Look at some examples where it can be used in real world.

In the area of research in human genetics, the goal is important to understand the relationship of correspondence between the inter-individual variations in human DNA sequences and the variability in susceptibility to disease. Simply put, it is how changes in the DNA sequence of an individual affect the risk of developing diseases such as cancer. This is very important for the diagnosis, prevention and treatment of diseases. The data mining technique used for this task is known as multifactor dimensionality reduction.

In the field of electrical engineering, data mining techniques are widely used for monitoring the condition of high voltage electrical equipment. The purpose of condition monitoring is to provide valuable information on the health of the insulation of the equipment. Data, such as the combination of self-organizing map (SOM) was applied to the vibration monitoring and analysis of transformer on-load tap changers (OLTC).

Using vibration monitoring, it may be noted that each tap change operation of a signal about the status of the contacts and generate trimmer disks. Of course, the tap positions generate different signals. However, there was considerable variability between the signals normally makes precisely the same function. SOM was used to detect abnormal conditions and the nature of the deviations to estimate.

Joseph Hayden writes article on Data Extraction Services, Web Data Extraction, Website Data Extraction, Web Screen Scraping, Web Data Mining, Web Data Extraction etc.