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The chalk module is a third-party library that can be used for styling of texts. It allows the users to create their own themes in a Node.js project.This module helps the users to customize the response messages with different colors as per the preferences.It also improves the readability by providing colors and makes it easier to detect warnings and errors.Installationnpm install chalkExample 1Create a file with the name "chalk.js" and copy the following code. After creating the file, use the command "node chalk.js" to run this code as shown in the example below −// Importing the chalk module const chalk=require("chalk"); ... Read More
The FASMI TestIt can represent the characteristics of an OLAP application in a specific method, without dictating how it should be performed.Fast − It defines that the system is targeted to produce most responses to users within about five seconds, with the understandable analysis taking no more than one second and very few taking more than 20 seconds.Independent research in the Netherlands has shown that end-users consider that a process has declined if results are not received with 30 seconds, and they are suitable to hit ‘ALT+Ctrl+Delete’ unless the system needs them that the report will take longer.Analysis − It ... Read More
A hierarchical clustering technique works by combining data objects into a tree of clusters. Hierarchical clustering algorithms are either top-down or bottom-up. The quality of an authentic hierarchical clustering method deteriorates from its inability to implement adjustment once a merge or split decision is completed.The merging of clusters is based on the distance among clusters. The broadly used measures for the distance between clusters are as follows, where mi is the mean for cluster Ci, ni is the number of points in Ci, and |p – p’| is the distance among two points p and p'.Types of Hierarchical Clustering MethodsThere ... Read More
The 'beforeExit' event is called when Node.js empties its event loop and has no other work to schedule. The Node.js process exits normally when there is no work scheduled but a listener registered on the 'before exit' event can make async calls and thereby cause the Node.js process to continue.Example 1Create a file with the name "beforeExit.js" and copy the following code. After creating the file, use the command "node beforeExit.js" to run this code as shown in the example below −// process 'beforeExit' Demo Example // Importing the process module const process = require('process'); // Calling the ... Read More
The diffieHellman.getGenerator() method returns the Diffie-Hellman generator in the specified encoding. A string is returned in case an encoding is passed, else a buffer is returned.SyntaxdiffieHellman.getGenerator([encoding])Parametersencoding - This parameter specifies the encoding of the return value.Example 1Create a file with the name "generator.js" and copy the following code. After creating the file, use the command "node generator.js" to run this code as shown in the example below:// diffieHellman.getPrime() Demo Example // Importing cryptoDiffieHellman from the crypto module const { createDiffieHellman } = require('crypto'); // Initializing the diffieHellman object const dh = createDiffieHellman(512); // Generate DiffieHellman's Generator ... Read More
The dnsPromises.resolveAny() method uses the DNS protocol to resolve all records (this is also known as ANY or * query). The promise is resolved with an array containing various types of records.TypeProperties'A'IPv4 address'AAAA'IPv6 address'Any'Any RecordsMXMail Exchange RecordsNAPTRName authority pointer recordsNSName server recordsPTRPointer RecordsSOAStart of Authority RecordsSRVService RecordsTXTText RecordsCNAMECanonical Name RecordsSyntaxdnsPromises.resolveAny(hostname)Parametershostname - This parameter takes input for the hostname to be resolved.Example 1Create a file with the name "resolveAny.js" and copy the following code. After creating the file, use the command "node resolveAny.js" to run this code, as shown in the example below −// Node.js program to demonstrate the // dnsPromises.resolveAny() method ... Read More
A statistical discordancy test analysis two hypotheses; a working hypothesis and a different hypothesis. A working hypothesis, H, is a statement that the entire data set of n objects comes from an initial distribution model, F, i.e., H: oi Î F, where i = 1, 2, n.The hypothesis is retained if there is no statistically important evidence supporting its rejection. A discordancy test checks whether an object oi is essentially large (or small) regarding the distribution F. Different test statistics have been proposed for use as a discordancy test, based on the available knowledge of the data.Suppose that some statistic ... Read More
There are various methods of clustering which are as follows −Partitioning Methods − Given a database of n objects or data tuples, a partitioning method assembles k partitions of the information, where each partition defines a cluster, and k < n. It can allocate the data into k groups, which can satisfy the following necessity −Each group must include a minimum of one object.Each object should apply to accurately one group.Given k, the number of partitions to construct, a partitioning method makes an initial partitioning. It then uses an iterative relocation method which attempts to improve the partitioning by transforming ... Read More
There are various applications of clustering which are as follows −Scalability − Some clustering algorithms work well in small data sets including less than 200 data objects; however, a huge database can include millions of objects. Clustering on a sample of a given huge data set can lead to biased results. There are highly scalable clustering algorithms are required.Ability to deal with different types of attributes − Some algorithms are designed to cluster interval-based (numerical) records. However, applications can require clustering several types of data, including binary, categorical (nominal), and ordinal data, or a combination of these data types.Discovery of ... Read More
There are various challenges of data mining which are as follows −Efficiency and scalability of data mining algorithms − It can effectively extract data from a large amount of data in databases, the knowledge discovery algorithms should be efficient and scalable to huge databases. Specifically, the running time of a data mining algorithm should be predictable and acceptable in huge databases. Algorithms with exponential or even channel-order polynomial complexity will not be of efficient use.Usefulness, certainty, and expressiveness of data mining results − The identified knowledge should exactly portray the contents of the database and be beneficial for specific applications. ... Read More