Answer:
The correct answer is False.
Explanation:
Responsibility Assignment Matrix (RAM) : The role of people describing the participation by the people and various organization and role in completing their project and tasks, also called as RACI (Responsible, Accountable, Consulted, Informed) matrix. Mainly responsible for Work breakdown structure (WBS).
It is not related from the above statement, don't maps any project's work.
Hence, the above statement is False.
Answer:
Digital literacy
Explanation:
Digital Literacy means having a current knowledge and understanding of computers, mobile devices, the web, and related technologies.
Brainliest plz
Answer:
Big Data
Explanation:
Big Data refers to data sets that are large, comprising of different varieties (structured and unstructured data types) and which cannot be processed by the day to day database management systems and computer software otherwise referred to as traditional software and techniques.
The term Big does not necessarily refer to a particular size that a dataset must attain but describes the nature of the dataset and the fact that traditional database management systems cannot be used to process them take for example
The datasets of the entire product list from amazon
K-means can be used for hierarchical clustering by creating a hierarchical tree structure. This is done by setting the number of clusters to be created, and then running the k-means clustering algorithm for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree. This process is repeated until the desired number of clusters has been created.
<h3>The Use of K-Means Clustering for Hierarchical Clustering</h3>
K-means clustering is a popular technique used in machine learning and data mining for partitioning data into clusters. It is a flat clustering algorithm, in which data points are grouped according to their similarity. While k-means clustering is suitable for partitioning data into a fixed number of clusters, it can also be used for hierarchical clustering. Hierarchical clustering is a clustering technique that creates a hierarchical tree structure, where each level of the tree is made up of clusters created by the k-means clustering algorithm.
The process of creating a hierarchical tree structure using k-means clustering is fairly straightforward. First, the number of clusters to be created is set, and then the k-means clustering algorithm is run for each level of the tree. For each level, the clusters created are then combined to form the next level of the tree until the desired number of clusters has been created. This process ensures that the clusters created are meaningful and have similar characteristics.
Learn more about k-means :
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