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Data mining is the process of analyzing data from different perspectives and summarizing it into useful and comprehensible information. Data mining services allow users to analyze data from many different dimensions, categorize it, and summarize the relationships identified.

Applications of Data Mining
Data Mining services are employed extensively in various industries like scientific research, businesses, banking sector, intelligence agencies and many others. Data mining procedures have come a long way since it was incepted. There have been phenomenal growth and development and various new tools and softwares have come up that makes the processes easier. In the present, the complex data mining techniques can be carried out with ease and results can be achieved more efficiently.

Businesses use data mining services to understand the buying behaviors of their customers and accordingly improve their marketing efforts. These services help to gauge the potential of and optimize processes like Attrition Analysis, Customer Segmentation and Cross Selling so that they deliver excellent revenue.

Data Mining is used in the banking sector as well for credit card fraud detection. It helps to identify the patterns involved in fraudulent transactions and keys in on the offender. It is also helps to reduce credit risk by classifying a potential client and predicting bad loans.

Data Mining techniques are also used by intelligence agencies like FBI and CIA to identify threats of terrorism.

Process of Data Mining
Data mining process comprises of several different steps. Here are a step-by-step guide to them:

Step 1 - Data Integration
All the data is collected and integrated from all the different sources. This creates a repository of information at one place.

Step 2 - Data Selection
All the data collected in the first step may not be correct or relevant. Therefore, the next step needs to scan through the data and collect only those data which seems to be useful for data mining.

Step 3 – Data Cleansing
The accumulated data would not be clean and may contain many errors, inconsistencies pertaining to missing values, noisy or inconsistent data. So, data cleansing activities are applied get rid of such inconsistencies and make sure that data is relevant and correct.

Step 4 – Data Transformation
After cleansing, the data is still not ready for miming purposes. This is because it might not be available in the relevant format. That is why data transformation steps are taken to transform data into other forms that are appropriate for mining. This may make use of several procedures like smoothing, aggregation, normalization etc.

Step 5 - Data Mining
Now we are ready to apply data mining techniques on the data to discover the interesting patterns. Techniques like clustering and association analysis are among the many different techniques used for data mining.

Step 6 - Pattern Evaluation and Knowledge Presentation
This step involves visualization, transformation of data and removing redundant patterns etc from the patterns we generated.

Step 7 - Use of Discovered Knowledge
This step helps users to make use of the knowledge acquired. Businesses can use the data to make their marketing strategies more effective.

Maneet Puri is the managing director of LeXolution IT Services, a reputed IT services firm that caters to clients from across the world. His company provides a range of KPO services including internet research, virtual assistance and data entry and data mining services.