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WaveCluster is a multiresolution clustering algorithm that first summarizes the records by imposing a multidimensional grid architecture onto the data space. It can use a wavelet transformation to change the original feature space, finding dense domains in the transformed space.In this method, each grid cell summarizes the data of a group of points that map into the cell. This summary data generally fit into the main memory for use by the multiresolution wavelet transform and the subsequent cluster analysis.A wavelet transform is a signal processing approach that decomposes a signal into multiple frequency subbands. The wavelet model can be used ... Read More
The grid-based clustering methods use a multi-resolution grid data structure. It quantizes the object areas into a finite number of cells that form a grid structure on which all of the operations for clustering are implemented. The benefit of the method is its quick processing time, which is generally independent of the number of data objects, still dependent on only the multiple cells in each dimension in the quantized space.An instance of the grid-based approach involves STING, which explores statistical data stored in the grid cells, WaveCluster, which clusters objects using a wavelet transform approach, and CLIQUE, which defines a ... Read More
The stringDecoder.end() method will return any remaining input that is left in the internal buffer as a string. Bytes that are left incomplete and represent UTF-8 and UTF-16 characters are replaced with the substitution characters appropriate for character encoding.StringDecoder.write() method is called before returning the remaining input if any buffer argument is provided. The stringDecoder can be reused for taking new inputs once the end() method is called.SyntaxstringDecoder.end( [buffer] )Parametersbuffer - This parameter takes input for the bytes to decode. It can take buffer, TypedArray or DataView as the input parameters.Example 1Create a file with the name "end.js" and copy ... Read More
The retry_strategy is a function that receives objects as parameters including the retry attempt, total_retry_time that indicates the time passed after it was connected the last time, the error due to which the connection was lost, and the number of times_connected in total.If a number is returned from this function, the next retry will take place after that time only in milliseconds and if you send a non-number, no further retry will take place.Syntaxretry_strategy: funciton(options)Example 1Create a file with the name "retryStrategy.js" and copy the following code. After creating the file, use the command "node retryStrategy.js" to run this code ... Read More
This process.noDeprecation() method states whether the --no- Deprecation flag is set or not on the current Node.js project. This Boolean flag controls whether the deprecation warning messages are printed to stderr or not. Setting this flag as True will silence all the deprecation warnings.Syntaxprocess.noDeprecation( )Example 1Create a file with the name "noDeprecation.js" and copy the following code. After creating the file, use the command "node noDeprecation.js" to run this code as shown in the example below// process.noDeprecation() Demo Example // Importing the process module const process = require('process'); // Printing noDeprecation default value console.log(process.noDeprecation);Output 1undefined Output 2trueExample 2Let's ... Read More
Chameleon is a hierarchical clustering algorithm that uses dynamic modeling to decide the similarity among pairs of clusters. It was changed based on the observed weaknesses of two hierarchical clustering algorithms such as ROCK and CURE.ROCK and related designs emphasize cluster interconnectivity while neglecting data regarding cluster proximity. CURE and related design consider cluster proximity yet neglect cluster interconnectivity. In Chameleon, cluster similarity is assessed depending on how well-connected objects are inside a cluster and on the proximity of clusters. Especially, two clusters are combined if their interconnectivity is high and they are close together.It does not base on a ... Read More
The dns.resolvePtr() method uses the DNS protocol to resolve the pointer records (PTR Records) for the hostname. The addresses argument passed to the callback function will contain the reply records as an array of strings.Syntaxdns.resolvePtr(hostname, callback)Parametershostname - This parameter takes the input for the hostname to be resolved.callback - This function will catch errors, if any.records − Returns PTR records for the hostname.Example 1Create a file with the name "resolvePtr.js" and copy the following code. After creating the file, use the command "node resolvePtr.js" to run this code as shown in the example below// dns.resolvePtr() Demo Example // Importing ... Read More
The dnsPromises.resolveSoa() method uses the DNS protocol to resolve the Start of Authority records (SOA Records) for the hostname. On success, the promise is resolved with the following propertiesnsnamehostmasterserialrefreshretryexpireminttlSyntaxdnsPromises.resolveSoa( hostname )Parametershostname - This parameter takes the input for the hostname to be resolved.Example 1Create a file with the name "resolveSoa.js" and copy the following code. After creating the file, use the command "node resolveSoa.js" to run this code as shown in the example below// dns.resolveSoa() Demo Example // Importing the dns module const dns = require('dns'); const dnsPromises = dns.promises; // Passing IP to find the hostname TXT ... Read More
A classic k-medoids partitioning algorithm like PAM works efficiently for small data sets but does not scale well for huge data sets. It can deal with higher data sets, a sampling-based method, known as CLARA (Clustering Large Applications), can be used.The approach behind CLARA is as follows: If the sample is chosen in a fairly random manner, it must closely define the original data set. The representative objects (medoids) chosen will be similar to those that would have been selected from the entire data set. CLARA draws several samples of the data set, applies PAM on each sample, and returns ... Read More
There are the following requirements of clustering in data mining which are as follows −Scalability − Some clustering algorithms work well on small data sets including fewer than some hundred data objects. A huge database can include millions of objects. Clustering on a sample of a given huge data set can lead to partial results. Highly scalable clustering algorithms are required.Ability to deal with different types of attributes − Some algorithms are designed to cluster interval-based (numerical) information. However, applications can require clustering several types of data, including binary, categorical (nominal), and ordinal data, or a combination of these data ... Read More