Data Mining Demystified

Data Mining or Predictive Analysis is an act of transferring data into beneficial Information and actionable insight. Also often known as Knowledge Discovery in Databases (KDD),

Data Mining is a mechanized process to uncover a series of never-before-seen information in bulk quantities of data scenario. Post evaluating a series of random factors, which the human mind cannot easily look at or comprehend, it helps in reaching towards an actionable insight by means of progressive mathematical algorithms.

These data mining reports are further distributed among esteemed influence rs and stakeholders, and are used for enterprise-caliber data mining observations in an insightful manner.

Below are few typical ways how companies can use Data Mining in various types of decisions.

ASSOCIATION: 
Affinity analysis and association rule learning encompasses a broad set of analytics techniques aimed at uncovering the associations and connections between specific objects: these might be visitors to your website (customers or audience), products in your store, or content items on your media site. Of these, “market basket analysis” is perhaps the most famous example. In a market basket analysis, you look to see if there are combinations of products that frequently co-occur in transactions.

For example, maybe people who buy flour and casting sugar, also tend to buy eggs (because a high proportion of them are planning on baking a cake). A retailer can use this information to inform: Store layout (put products that co-occur together close to one another, to improve the customer shopping experience) Marketing (e.g. target customers who buy flour with offers on eggs, to encourage them to spend more on their shopping basket) Online retailers and publishers can use this type of analysis to: Inform the placement of content items on their media sites, or products in their catalog Drive recommendation engines (like Amazon’s customers who bought this product also bought these products…) Deliver targeted marketing (e.g. emailing customers who bought products specific products with other products and offers on those products that are likely to be interesting to them.) 

CLASSIFICATION: 
Examining the properties of newly presented data and assigning it to one of the several predefined groups. Banks use this technique to identify whether a particular credit card transaction is fraudulent 

CLUSTERING: 
Identifying similarities and common ground between observations and groups. For instance, creating profiles for website users or clients by mapping website usage pattern and customer behavior. 

DESCRIPTION: 
Detailing out patterns and showcasing them in a visual manner using explanatory analysis. ESTIMATION: Revealing features that are difficult to observe with a straight-lined approach because of cost of observation or technical problems. 

PREDICTION: 
Predicting an estimated future using previous and present observations. Advantages of Data Mining Marketing / Retail Data mining helps marketing companies build models based on historical data to predict who will respond to the new marketing campaigns such as direct mail, online marketing campaign…etc. Through the results, marketers will have appropriate approach to sell profitable products to targeted customers. 

Finance / Banking Data mining gives financial institutions information about loan information and credit reporting. By building a model from historical customer’s data, the bank and financial institution can determine good and bad loans. In addition, data mining helps banks detect fraudulent credit card transactions to protect credit card’s owner. Manufacturing By applying data mining in operational engineering data, manufacturers can detect faulty equipment and determine optimal control parameters. For example semi-conductor manufacturers has a challenge that even the conditions of manufacturing environments at different wafer production plants are similar, the quality of wafer are lot the same and some for unknown reasons even has defects. Data mining has been applying to determine the ranges of control parameters that lead to the production of golden wafer. Then those optimal control parameters are used to manufacture wafers with desired quality. 

Governments Data mining helps government agency by digging and analyzing records of financial transaction to build patterns that can detect money laundering or criminal activities
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