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Articles on Trending Technologies
Technical articles with clear explanations and examples
What is sequential pattern mining?
Sequential pattern mining is the mining of frequently appearing series events or subsequences as patterns. An instance of a sequential pattern is users who purchase a Canon digital camera are to purchase an HP color printer within a month.For retail information, sequential patterns are beneficial for shelf placement and promotions. This industry, and telecommunications and different businesses, can also use sequential patterns for targeted marketing, user retention, and several tasks.There are several areas in which sequential patterns can be used such as Web access pattern analysis, weather prediction, production processes, and web intrusion detection.Given a set of sequences, where each ...
Read MoreWhat is STREAM?
STREAM is an individual-pass, constant element approximation algorithm that was produced for the k-medians problem. The k-medians problem is to cluster N data points into k clusters or groups such that the sum squared error (SSQ) between the points and the cluster center to which they are assigned is minimized. The idea is to assign similar points to the same cluster, where these points are dissimilar from points in other clusters.In the stream data model, data points can only be seen once, and memory and time are limited. It can implement high-quality clustering, the STREAM algorithm processes data streams in ...
Read MoreRight-justify elements of an array and set the characters to use for padding in Numpy
To right-justify elements of an array and set the characters to use for padding, use the numpy.char.rjust() method in Python Numpy. The "width" parameter is the length of the resulting strings. The "fillchar" parameter is the character to use for padding.The function returns an output array of str or unicode, depending on input type. The numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['Tom', 'John', 'Kate', 'Amy', 'Brad']) Displaying our array −print("Array...", arr)Get the datatype −print("Array ...
Read MoreWhat are the methodologies of data streams clustering?
Data stream clustering is described as the clustering of data that appar continuously including telephone data, multimedia data, monetary transactions etc. Data stream clustering is generally treated as a streaming algorithm and the objective is, given a sequence of points, to make a best clustering of the stream, utilizing a small amount of memory and time.Some applications needed the automated clustering of such data into set based on their similarities. Examples contains applications for web intrusion detection, analyzing Web clickstreams, and stock market analysis.There are several dynamic methods for clustering static data sets clustering data streams places additional force on ...
Read MoreReturn a string which is the concatenation of the strings in the sequence in Numpy
To return a string which is the concatenation of the strings in the sequence, use the numpy.char.join() method in Python Numpy. The 1st parameter is the separator array. The 2nd parameter is the sequence array. The function returns an output array of str or unicode, depending on input typesThe numpy.char module provides a set of vectorized string operations for arrays of type numpy.str_ or numpy.bytes_.StepsAt first, import the required library −import numpy as npCreate a One-Dimensional array of string −arr = np.array(['Bella\tCio', 'Tom\tHanks', 'Monry\tHeist\tSeries']) Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype) Get the dimensions of the Array ...
Read MoreReturn a 3-tuple for pickling a MaskedArray in Numpy
To return a 3-tuple for pickling a MaskedArray., use the ma.MaskedArray.__reduce__() method in Python Numpy. A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.StepsAt first, import ...
Read MoreReturn the internal state of the masked array for pickling purposes in Numpy
To return the internal state of the masked array for pickling purposes, use the ma.MaskedArray.__getstate__() method in Python Numpy.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array ...
Read MoreHow does the Lossy Counting algorithm find frequent items?
A user supports two input parameters including the min support threshold, σ, and the error bound previously, indicated as ε. The incoming stream is theoretically divided into buckets of width w = [1/ε].Let N be the current stream length, i.e., the number of items view so far. The algorithm needs a frequency-list data structure for all elements with frequency higher than 0. For every item, the list supports f, the approximate frequency count, and ∆, the maximum possible error of f.The algorithm procedure buckets of items as follows. When a new bucket arrives in, the items in the bucket are ...
Read MoreCopy and return all the elements of a masked array in Numpy
To copy all the elements of a masked array, use the ma.MaskedArray.__copy__() method in Python Numpy. A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.StepsAt first, ...
Read MoreReturn a boolean indicating whether the data is contiguous in Numpy
To return a boolean indicating whether the data is contiguous, use the ma.MaskedArray.iscontiguous() method in Python Numpy.A masked array is the combination of a standard numpy.ndarray and a mask. A mask is either nomask, indicating that no value of the associated array is invalid, or an array of booleans that determines for each element of the associated array whether the value is valid or not.NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. It supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries.StepsAt first, ...
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