Short Note on Datamining

-

 DATA MINING

  • Data mining refers to extracting knowledge from large amounts of data. The data sources can include databases, data warehouse, web, etc.
  • Data mining refers to the analysis of data. It is the computer-supported process of analyzing huge sets of data that have either been compiled by computer systems or have been downloaded into the computer.
  •  In the data mining process, the computer analyzes the data and extracts useful information from it. It looks for hidden patterns within the data set and tries to predict future behavior. Data mining is primarily used to discover and indicate relationships among the data sets.
  • Data mining aims to enable business organizations to view business behaviors, trends relationships that allow the business to make data-driven decisions.
  • Data mining is also called Knowledge Discovery in Database (KDD). The knowledge discovery process includes Data cleaning, Data integration, Data selection, Data transformation, Data mining, Pattern evaluation, and Knowledge presentation.
  • Data Mining is used to extract useful information and patterns from data. Data mining can be carried with any traditional database, but since a data warehouse contains quality data, it is good to have data mining over the data warehouse system. Data Mining supports knowledge discovery by finding hidden patterns and associations, constructing analytical models, performing classification and prediction.


OR,


Data Mining, like gold mining, is the process of extracting value from the data stored in the data warehouse. Data mining techniques include the process of transforming raw data sources into a consistent schema to facilitate analysis; identifying patterns in a given dataset and creating visualizations that communicate the most critical insights. There is hardly a sector of commerce, science, or technology that isn’t using data mining and data mining techniques to accelerate its objectives. From fraud detection and inventory management to cancer research and marketing campaigns, data mining is changing decision-making around the globe.


Comments

Post a Comment

Popular posts from this blog

Discuss classification or taxonomy of virtualization at different levels.

-
Image
Taxonomy of virtualization Virtualization is mainly used to emulate the execution environment, storage, and networks. The execution environment is classified into two: – Process-level – implemented on top of an existing operating system.  – System-level – implemented directly on hardware and does not or minimum requirement of the existing operating system. OR,  Virtualization covers a wide range of emulation techniques that are applied to different areas of computing. A classification of these techniques helps us better understand their characteristics and use  V irtualization is mainly used to emulate  ● Execution Environments: To provide support for the execution of the programs eg. OS, and Application.  ○ Process Level: Implemented on top of an existing OS that has full control of the hardware  ○ System Level: Implemented directly on Hardware and do not require support from existing OS.  ● Storage: Storage vi...
Read more

What is RMI? Discuss stub and skeleton. Explain its role in creating distributed applications.

-
 The RMI (Remote Method Invocation) is an API that provides a mechanism to create distributed application in java. The RMI allows an object to invoke methods on an object running in another JVM. The RMI provides remote communication between the applications using two objects stub and skeleton. RMI applications often comprise two separate programs, a server and a client. A typical server program creates some remote objects, makes references to these objects accessible, and waits for clients to invoke methods on these objects. A typical client program obtains a remote reference to one or more remote objects on a server and then invokes methods on them.  RMI provides the mechanism by which the server and the client communicate and pass information back and forth. A primary goal for the RMI designers was to allow programmers to develop distributed Java programs with the same syntax and semantics used for non-distributed programs. To do this, they had to carefully map how Java clas...
Read more

Suppose that a data warehouse consists of the three dimensions time, doctor, and patient, and the two measures count and charge, where a charge is the fee that a doctor charges a patient for a visit. a) Draw a schema diagram for the above data warehouse using one of the schemas. [star, snowflake, fact constellation] b) Starting with the base cuboid [day, doctor, patient], what specific OLAP operations should be performed in order to list the total fee collected by each doctor in 2004? c) To obtain the same list, write an SQL query assuming the data are stored in a relational database with the schema fee (day, month, year, doctor, hospital, patient, count, charge)

-
Image
Solution: a) Star Schema is shown in the figure below: b) First, we should use the roll-up operation to get the year 2004(rolling-up from the day then month to a year). After getting that, we need to use the slice operation to select (2004). Second, we should use roll-up operation again to get all patients. Then, we need to use the slice operation to select (all). Finally, we get a list of the total fee collected by each doctor in 2004. So, 1. roll up from day to month to year 2. slice for year = “2004” 3. roll up on patient from the individual patient to all 4. slice for patient = “all” 5. get the list of the total fees collected by each doctor in 2004 c) Select doctor, Sum(charge) From fee Where year = 2004 Group by doctor
Read more