Data Mining

Data Mining

Introduction

Huge data stores are searched automatically in the practice of data mining. This helps in discovering trends and patterns that are complicated to analyze. Sophisticated algorithms based on mathematics are used in data mining in order to assess the possibility of upcoming events and also for data segmentation. Knowledge Discovery in Data is another name of data mining (Oracle, 2005).

Benefits of Data Mining

Predictive Analytics in Data Mining

Huge amount of data is analyzed in data mining while employing predictive analytics. This is accomplished by the extraction of correlations and patterns by the application of statistical or mathematical models or else hidden. The analytic tools have emerged the businesses and they act as core business drivers of competitive advantage and institutional education for the past decade (Fuzion Analytics, 2012).

Success in LTC i.e. long term care like other insurance industries depends on the way correctly carriers can manage and quantify enterprise risk. It also depends on the degree of embedding these practices into core processes of business. The implementation of advanced techniques of data analytics have become valuable and useful tool in the applications such as reserving, fraud detection and risk management. These techniques help to manage the complications that are incorporated with increasing consumer expectations and rising regulatory oversight. This produces sweeping effects on carriers. Due to the complications that are possible to occur in long term care and also due to the considerable data volumes that are needed to analyze and identify risk factors of organization, carriers will be greatly beneficial form implementing analytic practices for driving data across the firm. By the help of predictive analytics in data mining, business drivers and risk factors can be reliably and efficiently prioritized and quantified, permitting carriers to acquire actionable insight that helps in transforming systematic risk into opportunity (Fuzion Analytics, 2012).

Association Discovery

The behavior of particular processes or events can be identified by the use of association analysis. Associations link incidences in a single event or process. There is an example which illustrates the Associations discovery that men who buy best coffee brands are 3 times more probable to purchase imported cigars as compared to men who purchase normal brands of coffee.

Associations Analysis follows a rule which identifies that: if product B is event's part then Y percent of the time product C is same event's part. Following are the association analysis examples:

If the customer purchases the snacks then there is the probability of 85 % for purchasing beer or soft drinks.

If the customer purchases airline tickets for their vacations for their family members, then there is the probability of 95 % that customer will hire a full-sized car on rent at his or her location of vacation.

Retail stores and super markets can create hypotheses for the purchasing trends and patterns by the use of scanners in grocery stores. Market basket analysis is another name of association analysis because it has been widely implemented in grocery stores (Moss, 2003).

Web Mining

Web mining is the technique which is used for ...