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Articles by Ginni
Page 86 of 124
What is the basic method of attribute subset selection?
Attribute subset selection decreases the data set size by eliminating irrelevant or redundant attributes (or dimensions). Attribute subset selection aims to discover a minimum set of attributes such that the resulting probability distribution of the data classes is as close as applicable to the original distribution accessing using all attributes. Data mining on a reduced set of attributes has an extra benefit. It reduces the multiple attributes occurring in the discovered patterns, provides to create the patterns simpler to understand.For n attributes, there are 2n possible subsets. An exhaustive search for the optimal subset of attributes can be intensely expensive, ...
Read MoreWhat is Data Reduction?
Data mining is applied to the selected data in a large amount database. When data analysis and mining is done on a huge amount of data then it takes a very long time to process, which makes it impractical and infeasible. It can reduce the processing time for data analysis, data reduction techniques are used to obtain a reduced representation of the dataset that is much smaller in volume by maintaining the integrity of the original data. By reducing the data, the efficiency of the data mining process is improved which produces the same analytical results.Data reduction aims to define ...
Read MoreWhat is Data Transformation?
In data transformation, the data are transformed or combined into forms suitable for mining. Data transformation can involve the following −Smoothing − It can work to remove noise from the data. Such methods contain binning, regression, and clustering.Aggregation − In aggregation, where summary or aggregation operations are applied to the data. For example, the daily sales data may be aggregated to compute monthly and annual total amounts. This phase is generally used in making a data cube for the analysis of the data at multiple granularities.Generalization − In Generalization, where low-level or “primitive” (raw) data are restored by larger-level concepts ...
Read MoreWhat is Data Cleaning?
Data cleaning defines to clean the data by filling in the missing values, smoothing noisy data, analyzing and removing outliers, and removing inconsistencies in the data. Sometimes data at multiple levels of detail can be different from what is required, for example, it can need the age ranges of 20-30, 30-40, 40-50, and the imported data includes birth date. The data can be cleans by splitting the data into appropriate types.Types of data cleaningThere are various types of data cleaning which are as follows −Missing Values − Missing values are filled with appropriate values. There are the following approaches to ...
Read MoreWhat is the motivation behind data mining?
Data mining is the procedure of finding useful new correlations, patterns, and trends by sharing through a high amount of data saved in repositories, using pattern recognition technologies including statistical and mathematical techniques. It is the analysis of factual datasets to discover unsuspected relationships and to summarize the records in novel methods that are both logical and helpful to the data owner.It is the procedure of selection, exploration, and modeling of high quantities of information to find regularities or relations that are at first unknown to obtain clear and beneficial results for the owner of the database.It is not limited ...
Read MoreWhat Kinds of data can be mined?
Data mining defines extracting or mining knowledge from huge amounts of data. Data mining is generally used in places where a huge amount of data is saved and processed. For example, the banking system uses data mining to save huge amounts of data which is processed constantly.In Data mining, hidden patterns of data are considering according to the multiple categories into a piece of useful data. This data is assembled in an area including data warehouses for analyzing it, and data mining algorithms are performed. This data facilitates in creating effective decisions which cut value and increase revenue.There are various ...
Read MoreWhat is the difference between Business Intelligence and Predictive Analytics?
Let us begin by learning about business intelligence.Business IntelligenceBusiness intelligence is an application-driven phase allowing organizations to analyze raw information from various sources, extracting vision that lead to higher effective business results. It is a set of tools and methods that analyze and transform raw information into actionable and coherent data for use in business analysis to support decision-making.Each business has strong transaction-oriented systems that save all information gathered from daily operations into repositories.It can stay competitive, businesses should rediscover and uses the data they influence, and this is where BI appears into play. Business intelligence can change vision from ...
Read MoreConvert the following LEX program into Lexical Analyzer.nAUXILIARY DEFINITIONSn −n −n −nTRANSLATION RULESn a{ }n abb{ }n a*b+
SolutionConvert the pattern into NFA’sMake a Combined NFAConvert NFA to DFAA = ε − closure (0) = {0, 1, 3, 7}The transition on symbols a, b from state AFor State Aε − closure (Ta) ε − closure (Tb)= ε − closure ({2, 4, 7}) = ε − closure ({8})= {2, 4, 7} = B = {8} = CFor State Bε − closure (7) ...
Read MoreWhat is Implementation of Block Structured Language in compiler design?
A block is a statement containing its own local data declaration. The concept of a block is originated with ALGOL. The block-structured language permits an array with adjustable length. The main feature of blocks is their bracketing structure (begin and end used in ALGOL) in which they can define their data.Activation Record for Block Structured LanguagesBlock structured language like ALGOL, and PL/I permit adjustable arrays, i.e., of varying length. Therefore, we cannot store irregular size arrays in between activation records. It can allocate the flexible or variable arrays at one corner of the activation record or above the fixed-size data. ...
Read MoreWhat is syntax-directed translation schemes in compiler design?
It is a kind of notation in which each production of Context-Free Grammar is related with a set of semantic rules or actions, and each grammar symbol is related to a set of Attributes. Thus, the grammar and the group of semantic Actions combine to make syntax-directed definitions.The translation may be the generation of intermediate code, object code, or adding the information in symbol table about constructs type. The modern compiler uses the syntax-directed translation that makes the user’s life easy by hiding many implementation details and free the user from having to specify explicitly the order in which semantic ...
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