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Articles on Trending Technologies
Technical articles with clear explanations and examples
What are the challenges of Outlier detection?
An outlier is a data object that deviates essentially from the rest of the objects, as if it were produced by a different structure. For ease of presentation, it can define data objects that are not outliers as “normal” or expected information. Similarly, it can define outliers as “abnormal” data.Outliers are data components that cannot be combined in a given class or cluster. These are the data objects which have several behaviour from the general behaviour of different data objects. The analysis of this kind of data can be important to mine the knowledge.There are various challenges of outlier detection ...
Read MoreWhat are the types of Outliers in data mining?
There are various types of outliers in data mining are as follows −Global Outliers − In a given data set, a data object is a global outlier if it deviates essentially from the rest of the information set. Global outliers are known as point anomalies, and are the easiest type of outliers. Most outlier detection methods are aimed at discovering global outliers.It can identify global outliers, an important issue is to discover an appropriate measurement of deviation concerning the application in question. There are several measurements are proposed, and, depends on these, outlier detection approaches are partitioned into multiple categories.Global ...
Read MoreWhat are the methods for Clustering with Constraints?
There are various techniques are required to handle specific constraints. The general principles of handling hard and soft constraints which are as follows −Handling Hard Constraints − A general methods for handling difficult constraints is to strictly regard the constraints in the cluster assignment procedure. Given a data set and a group of constraints on examples (i.e., must-link or cannot-link constraints), how can we develop the k-means approach to satisfy such constraints? The COP-kmeans algorithm works as follows −Generate super instances for must-link constraints − It can calculate the transitive closure of the must-link constraints. Therefore, all must-link constraints are ...
Read MoreHow can we measure the similarity or distance between two vertices in a graph?
There are two types of measures such as geodesic distance and distance based on random walk.Geodesic Distance − A simple measure of the distance among two vertices in a graph is the shortest route among the vertices. Usually, the geodesic distance among two vertices is the length in terms of the multiple edges of the shortest path among the vertices. For two vertices that are not linked in a graph, the geodesic distance is represented as infinite.By utilizing geodesic distance, it can represent various useful measurements for graph analysis and clustering. Given a graph G = (V, E), where V ...
Read MoreWhat are the Categorization of Constraints in data mining?
Constraint-based algorithms need constraints to decrease the search area in the frequent itemset generation phase (the association rule creating step is exact to that of exhaustive algorithms).The importance of constraints is well-defined and they make only association rules that are interesting to customers. The method is quite trivial and the rules area is decreased whereby remaining rules use the constraints.There are three types of constraints which are as follows −Constraints on instances − A constraint on instances defines how a pair or a set of instances must be grouped in the cluster analysis. There are two types of constraints from ...
Read MoreMask rows and/or columns of a 2D array that contain masked values along axis 0 in Numpy
To mask rows and/or columns of a 2D array that contain masked values, use the np.ma.mask_rowcols() method in Numpy. The function returns a modified version of the input array, masked depending on the value of the axis parameter.Mask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter −If axis is None, rows and columns are masked.If axis is 0, only rows are masked.If axis is 1 or -1, only columns are masked.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreate an array with ...
Read MoreMask rows and/or columns of a 2D array that contain masked values along axis 1 in Numpy
To mask rows and/or columns of a 2D array that contain masked values, use the np.ma.mask_rowcols() method in Numpy. The function returns a modified version of the input array, masked depending on the value of the axis parameterMask whole rows and/or columns of a 2D array that contain masked values. The masking behavior is selected using the axis parameter −If axis is None, rows and columns are masked.If axis is 0, only rows are masked.If axis is 1 or -1, only columns are masked.StepsAt first, import the required library −import numpy as np import numpy.ma as maCreate an array with ...
Read MoreCompare and return True if a Numpy array is greater than equal to another
To compare and return True if an array is greater than equal to another, use the numpy.char.greater_equal() method in Python Numpy. The arr1 and arr2 are the two input string arrays of the same shape.Unlike numpy.greater_equal, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray.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 two One-Dimensional arrays of string −arr1 = np.array(['Bella', 'Tom', 'John', 'Kate', 'Amy', 'Brad', 'aaa']) arr2 = ...
Read MoreReturn an array with the elements of a Numpy array right-justified in a string of length width
To return an array with the elements of an array right-justified in a string of length width, use the numpy.char.rjust() method in Python Numpy. The "width" parameter is the length of the resulting strings.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 np# Create a One-Dimensional array of stringarr = np.array(['Tom', 'John', 'Kate', 'Amy', 'Brad'])Displaying our array −print("Array...", arr)Get the datatype −print("Array datatype...", arr.dtype)Get the dimensions of the Array −print("Array ...
Read MoreLeft-justify elements of an array and set the characters to use for padding in Numpy
To left-justify elements of an array and set the characters to use for padding, use the numpy.char.ljust() 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 datatype...", ...
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