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CluStream is an algorithm for the clustering of evolving data streams based on userspecified, online clustering queries. It divides the clustering process into on-line and offline components.The online component computes and stores summary statistics about the data stream using micro-clusters, and performs incremental online computation and maintenance of the micro-clusters. The offline component does macro-clustering and answers various user questions using the stored summary statistics, which are based on the tilted time frame model.The cluster evolving data streams based on both historical and current stream data information, the tilted time frame model (such as a progressive logarithmic model) is adopted, ... Read More
The Hoeffding tree algorithm is a decision tree learning method for stream data classification. It was initially used to track Web clickstreams and construct models to predict which Web hosts and Web sites a user is likely to access. It typically runs in sublinear time and produces a nearly identical decision tree to that of traditional batch learners.It uses Hoeffding trees, which exploit the idea that a small sample can often be enough to choose an optimal splitting attribute. This idea is supported mathematically by the Hoeffding bound (or additive Chernoff bound).Suppose we make N independent observations of a random ... Read More
BIRCH represents Balanced Iterative Reducing and Clustering Using Hierarchies. It is designed for clustering a huge amount of numerical records by integration of hierarchical clustering and other clustering methods including iterative partitioning.BIRCH offers two concepts, clustering feature and clustering feature tree (CF tree), which are used to summarize cluster description. These structures facilitate the clustering method to achieve the best speed and scalability in huge databases and also create it effective for incremental and dynamic clustering of incoming objects.Given n d-dimensional data objects or points in a cluster, and it can represent the centroid x0, radius R, and diameter D ... Read More
An object o in a data set S is a distance-based (DB) outlier with parameters p and d, i.e., DB (p, d), if minimum a fraction p of the objects in S lie at a distance higher than d from o. In other words, instead of depending on statistical tests, it can think of distance-based outliers as those objects who do not have enough neighbors.The neighbors are represented based on distance from the given object. In comparison with statistical-based methods, distance-based outlier detection generalizes or merges the ideas behind discordancy testing for standard distributions. Hence, a distance-based outlier is also ... Read More
Conventional Software Engineering PrinciplesThere are several descriptions of "old-school" engineering software. The software industry has learned many lessons and established numerous principles during the years of software development. This part introduces the fundamental ideas explored throughout the rest of the book by describing one perspective on today's software engineering principles. A small paper titled "Fifteen Principles of Software Engineering" [Davis, 1994] is the benchmark I've chosen. The paper was then expanded into a book [Davis, 1995], which lists 201 principles. Despite its title, the piece outlines the top 30 principles, and it's as excellent a description as any of the ... Read More
Penetration TestingPenetration testing, often known as pen testing, is a sort of security testing which is used to find flaws, hazards, and dangers that an intruder may abuse in software applications, networks, or online applications. The goal of penetration testing is to find and evaluate all potential security flaws in a software program. Pen Test is another name for penetration testing.The chance that an intruder would damage or obtain unauthorized access to a network or any data held inside it is referred to as vulnerability. Vulnerabilities are typically found by chance during the software planning and implementation phases. Common threats ... Read More
What exactly is Non-Destructive Software Testing?Non Destructive Testing is a sort of software testing that comprises evaluating and appropriately engaging with the software program. To put it another way, Non-Destructive Software Testing (NDT) is also known as Positive Testing or Happy Path Testing. It produces the required outcomes and demonstrates that the software program behaves as predicted.Putting the necessary info into a login module and testing to see if it recognizes credentials and moves onto another step is an example.In the previous example, input numeric characters in the username text field to undertake non-destructive testing. As a result of entering ... Read More
What is the definition of software development?Phones, televisions, automobiles, vending machines, coffee makers, even pet toys all have software-based functions these days. All of these items were developed by groups of people who banded together with the purpose of causing electrical signals to behave in a predictable manner. In a nutshell, software development refers to the entire process of bringing a software project from idea to completion.A software deliverable's design, documentation, programming, testing, and continuing maintenance are all part of this process. These elements are combined to form a workflow pipeline, which is a set of actions that, when followed, ... Read More
Plan for the testA Test Plan is a thorough document that outlines the test strategy, objectives, timetable, estimation, deliverables, and resources needed to accomplish software testing. The Test Plan assists us in determining the amount of work required to confirm the quality of the application being tested. The test plan is a blueprint for conducting software testing operations as a defined procedure, which the test manager closely monitors and controls."A test plan is a document detailing the scope, strategy, resources, and timetable of expected test activities, " according to the ISTQB definition.Let's look at an example/scenario of a Test Plan: ... Read More
The scipy.cluster.hierarchy module provides functions for hierarchical clustering and its types such as agglomerative clustering. It has various routines which we can use to −Cut hierarchical clustering into the flat clustering.Implement agglomerative clustering.Compute statistics on hierarchiesVisualize flat clustering.To check isomorphism of two flat cluster assignments.Plot the clusters.The routine scipy.cluster.hierarchy.fcluster is used to cut hierarchical clustering into flat clustering, which they obtain as a result an assignment of the original data point to single clusters. Let’s understand the concept with the help of below given example −Example#Importing the packages from scipy.cluster.hierarchy import ward, fcluster from scipy.spatial.distance import pdist #The cluster ... Read More