- Is a comparison of the general features of the target class of data?
- What is the strategic value of data mining?
- What is a good alternative to the star schema?
- Which one is not a data reduction strategy?
- What is the output of KDD process?
- Which of the following is data mining tool?
- What are the methods used for data reduction in data mining?
- What are the two main objectives associated with data mining?
- What is KDD process model?
- What are data preprocessing techniques?
- What is the heart of KDD in database?
- What is data mining when viewed as a process of knowledge discovery?
- Which essential process makes use of the intelligent methods for the extraction of data patterns?
- Is the application of data mining techniques to discover patterns from the Web?
- What is the function of data mining?
Is a comparison of the general features of the target class of data?
Data discrimination is a comparison of the general features of the target class data objects against the general features of objects from one or multiple contrasting classes.
The target and contrasting classes can be specified by a user, and the corresponding data objects can be retrieved through database queries..
What is the strategic value of data mining?
Discussion ForumQue.Strategic value of data mining isb.Time sensitivec.System sensitived.Technology sensitiveAnswer:Time sensitive1 more row
What is a good alternative to the star schema?
Final Thoughts. Star schemas are the simplest and most popular way of organizing information within a data warehouse. However, alternatives to the star schema, such as snowflake schemas and galaxy schemas, exist for users who will get more benefits from modeling their data warehouse in a different way .
Which one is not a data reduction strategy?
Discussion ForumQue.Which one is not a data reduction strategyb.Dimension reductionc.Data compressiond.Data cube aggregationAnswer:Data Generalization1 more row
What is the output of KDD process?
The main objective of the KDD process is to extract information from data in the context of large databases. It does this by using Data Mining algorithms to identify what is deemed knowledge. The Knowledge Discovery in Databases is considered as a programmed, exploratory analysis and modeling of vast data repositories.
Which of the following is data mining tool?
As a result, we have studied Data Mining Tools and Techniques are Rapid Miner, Orange, Weka, KNIME, Sisense, SSDT, Apache Mahout, Oracle Data Mining, Rattle, DataMelt, IBM Cognos, IBM SPSS Modeler, SAS Data Mining, Teradata, Board, Dundas BI, Python, Spark, and H20.
What are the methods used for data reduction in data mining?
Encoding techniques (Run Length Encoding) allows a simple and minimal data size reduction. Lossless data compression uses algorithms to restore the precise original data from the compressed data. Methods such as Discrete Wavelet transform technique, PCA (principal component analysis) are examples of this compression.
What are the two main objectives associated with data mining?
The mission of every data analysis specialist is to achieve successfully the two main objectives associated with data mining i.e. to find hidden patterns and trends.
What is KDD process model?
The term Knowledge Discovery in Databases, or KDD for short, refers to the broad process of finding knowledge in data, and emphasizes the “high-level” application of particular data mining methods. … The unifying goal of the KDD process is to extract knowledge from data in the context of large databases.
What are data preprocessing techniques?
Data preparation includes data cleaning, data integration, data transformation, and data reduction. Data cleaning routines can be used to fill in missing values, smooth noisy data, identify outliers, and correct data inconsistencies. Data integration combines data from multiples sources to form a coherent data store.
What is the heart of KDD in database?
Data Mining(DM) is the core of the KDD process, involv- ing the inferring of algorithms that explore the data, develop the model and discover previously unknown patterns.
What is data mining when viewed as a process of knowledge discovery?
Data Mining also known as Knowledge Discovery in Databases, refers to the nontrivial extraction of implicit, previously unknown and potentially useful information from data stored in databases. Data Cleaning: Data cleaning is defined as removal of noisy and irrelevant data from collection.
Which essential process makes use of the intelligent methods for the extraction of data patterns?
Knowledge Discovery (an essential process in which intelligent methods are used to extract data patterns) Pattern evolution (to identify truly interesting patterns that represent knowledge based on some interesting actions) Knowledge presentation (where an overview of visualization techniques and knowledge is used to …
Is the application of data mining techniques to discover patterns from the Web?
– Web usage mining Web usage mining is the application of data mining techniques to discover patterns using the Web to better understand and meet the needs of the user. … For example, web content mining techniques can use user information in addition to using the documents.
What is the function of data mining?
Data mining involves exploring and analyzing large blocks of information to glean meaningful patterns and trends. It can be used in a variety of ways, such as database marketing, credit risk management, fraud detection, spam Email filtering, or even to discern the sentiment or opinion of users.