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How Can You Remove Noisy Data In Data Mining?

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By Author: James Church
Total Articles: 4
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What do you mean by noisy data?

The data are said to be noisy when it delivers no meaning. In other words, it is known as redundant or corrupt data. However, you have web extraction tools, such as import.io or scraper, to churn the requisite data through its warehouses. But, every data extraction tool or program is not codified in a universal format. Actually, it’s very difficult to create an all-embracing program for filtering noisy data from diversely unique data-structure over the internet.

Thereby, the software fails to understand and interpret that data correctly. Such kind of data is considered noisy.
What causes noisy data?
• Hardware failure
• Programming errors
• Non sensible input from speech recognition or OCR
• Typo errors or data entry errors
• Different data dictionary of similar entities in different warehouses
• Abnormal data, like lots of abbreviations and slang

Why do you require removing noisy data?
The unstructured, illegible and redundant data put several barriers in extracting valuable information. Thereby, intelligence or decision-making remains pending. Business intelligence, being delayed, causes a massive loss because errors continue to disturb operations and productivity.
• Occupy unnecessary space
• Adversely impact the results of data mining during analysis
• Lead to inaccurate decisions
• Plenty of money, time and efforts go waste in sifting through such data

How can you remove the corrupt or noisy data?
Weeding corrupt data out is a critical obligation. But to keep bad decisions and breed breakthroughs, you ought to carry out data cleansing when it comes to providing data mining soluions. So basically, this cleansing determines detecting and correcting anomalies or inaccuracies from a database, table or record set. This process inserts consistency and viability into the data, which translates it into meaningful information. In the meantime, the cleansing process undergoes data validation, enhancement and standardization.

Data validation: There might have discrepancies, such as incorrect postal code. Such anomalies are eliminated by deploying inputs in the direction validating data.

Data enhancement: The partially matching records often feed wrong information. Data enhancement process integrates related data to serve complete information. Let’s say, you have addresses of leading hoteliers in Dubai. But, their postal codes are missing. You can enhance its value through enriching it with postal codes.
Data standardization: It deals with harmonizing short forms, such as St. into Street and rd. into road.

The aforementioned processes help in catering the cleansed data to output accurate information during data mining & the management process in research. Besides, there are some more steps that define its entire procedure or process.

1. Auditing: Conducting an official inspection of data defines it auditing. Multiple outsourcing data mining solutions providers rely on statistics, for example-regression algorithms and clustering, and database methods to spotlight anomalies and contradictions. The software like JavaScript or VB ensure the specification of constraints.

2. Workflow specification: Pre-defining a sequence of tasks makes it easy to perform, as the directions are invariably there to follow up. It is termed as workflow. After auditing, it specifies the starting and finishing line to achieve high-quality data.

3. Execution: The ultimate aim of cleansing is to act upon errors and incompletion. An experienced team of data entry and quality analysts constitute a back office staff to follow the hierarchical validation and verification efficiently.

4. Quality check: The valid, verified and enriched data qualify a high quality. The quality pushes that dataset for post-processing. To define quality, the data are passed through a series of criteria. These criteria consist of validity (including data-type constraints, range constraints, mandatory constraints, unique constraints, set-membership constraints, foreign-key constraints, regular expression patterns and cross-field validation), accuracy, completeness, consistency, uniformity and integrity. Once the cleaned data are pushed across quality check, the anomalies are sent for rectification manually.

5. Post-processing: Again, the deep cleansing steers data to the next level. It’s the auditing round again to examine whether or not that data match specified criteria. If required, the automatic processing for cleansing gears up.

During post-processing, the decision makers and back office staff focus on the “data quality culture”. It refers to the practice of decision makers to concentrate on the information inspired from general, economic or social market trends, sales volume of products and the performance of staff.

More About the Author

James is a business analyst with over five years of experience. He inclines toward big data for deriving incredible intelligence. Their implementation injects breakthroughs, which steer an operation from loss-bearing to profit-making scenario. He has written several success stories during outsourcing data solutions for innumerable clients.

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