- What is a good alternative to the star schema?
- What is classification in data mining?
- What is missing data in data mining?
- How can data mining remove noisy data?
- What causes noisy data?
- What is the KDD process?
- How is sound data calculated?
- How does data mining deal with missing values?
- How do you handle noisy data?
- What is noisy data in data mining?
- What is output of KDD?
- What is binning in data mining?
- What is the heart of KDD in database?
- What is noise in big data?
What is a good alternative to the star schema?
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 ..
What is classification in data mining?
Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. … A predictive model with a numerical target uses a regression algorithm, not a classification algorithm.
What is missing data in data mining?
A missing value can signify a number of different things in your data. Perhaps the data was not available or not applicable or the event did not happen. It could be that the person who entered the data did not know the right value, or missed filling in. Data mining methods vary in the way they treat missing values.
How can data mining remove noisy data?
Smoothing, which works to remove noise from the data. Techniques include binning, regression, and clustering. 2. Attribute construction (or feature construction), where new attributes are con- structed and added from the given set of attributes to help the mining process.
What causes noisy data?
The main causes of noisy data are objects that reflect or intermittently obstruct the signals from one or more of the satellites in view. Such obstacles are usually trees or buildings.
What is the KDD process?
KDD refers to the overall process of discovering useful knowledge from data. It involves the evaluation and possibly interpretation of the patterns to make the decision of what qualifies as knowledge.
How is sound data calculated?
1 AnswerSubtract a sample value from the average.Square that new value.Sum all the squared values.Divide the total by the number of samples.Take the square root.
How does data mining deal with missing values?
Data Mining — Handling Missing Values the DatabaseIgnore the data row. … Use a global constant to fill in for missing values. … Use attribute mean. … Use attribute mean for all samples belonging to the same class. … Use a data mining algorithm to predict the most probable value.
How do you handle noisy data?
The simplest way to handle noisy data is to collect more data. The more data you collect, the better will you be able to identify the underlying phenomenon that is generating the data. This will eventually help in reducing the effect of noise.
What is noisy data in data mining?
Noisy data are data with a large amount of additional meaningless information in it called noise. This includes data corruption and the term is often used as a synonym for corrupt data. It also includes any data that a user system cannot understand and interpret correctly.
What is output of KDD?
Answer: (d) The output of KDD is useful information. Q19. Which one is a data mining function that assigns items in a collection to target categories or classes.
What is binning in data mining?
Binning, also called discretization, is a technique for reducing the cardinality of continuous and discrete data. Binning groups related values together in bins to reduce the number of distinct values.
What is the heart of KDD in database?
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.
What is noise in big data?
Noise is the corruption – the partial or complete alteration – of the information gathered in a dataset, and it is one of the most frequent problems that affect datasets. It is caused by external factors during such processes as data acquisition, transmission, storage, integration and categorisation.