An Introduction to Distance Education: Garrison 0 Rezensionen https:

Overview Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters.

Suppose, you are the head of a rental store and wish to understand preferences of your costumers to scale up your business. Is it possible for you to look at details of each costumer and devise a unique business strategy for each one of them?

And this is what we call clustering. Now, that we understand what is clustering. Types of Clustering Broadly speaking, clustering can be divided into two subgroups: In hard clustering, each data point either belongs to a cluster completely or not.

In soft clustering, instead of putting each data point into a separate cluster, a probability or likelihood of that data point to be in those clusters is assigned.

For example, from the above scenario each costumer is assigned a probability to be in either of 10 clusters of the retail store. Types of clustering algorithms Since the task of clustering is subjective, the means that can be used for achieving this goal are plenty.

In fact, there are more than clustering algorithms known. As the name suggests, these models are based on the notion that the data points closer in data space exhibit more similarity to each other than the data points lying farther away. These models can follow two approaches. In the second approach, all data points are classified as a single cluster and then partitioned as the distance increases.

Also, the choice of distance function is subjective. These models are very easy to interpret but lacks scalability for handling big datasets. Examples of these models are hierarchical clustering algorithm and its variants. These are iterative clustering algorithms in which the notion of similarity is derived by the closeness of a data point to the centroid of the clusters.

K-Means clustering algorithm is a popular algorithm that falls into this category. In these models, the no. These models run iteratively to find the local optima. These clustering models are based on the notion of how probable is it that all data points in the cluster belong to the same distribution For example: These models often suffer from overfitting.

A popular example of these models is Expectation-maximization algorithm which uses multivariate normal distributions.

These models search the data space for areas of varied density of data points in the data space. It isolates various different density regions and assign the data points within these regions in the same cluster. Now I will be taking you through two of the most popular clustering algorithms in detail — K Means clustering and Hierarchical clustering.

K Means Clustering K means is an iterative clustering algorithm that aims to find local maxima in each iteration. This algorithm works in these 5 steps: Specify the desired number of clusters K: Randomly assign each data point to a cluster: The centroid of data points in the red cluster is shown using red cross and those in grey cluster using grey cross.

Re-assign each point to the closest cluster centroid:This article is an introduction to clustering and its types. they start with classifying all data points into separate clusters & then aggregating them as the distance decreases. In the second approach, all data points are classified as a single cluster and then partitioned as the distance increases.

He loves to use machine learning and. Distance education or long-distance learning is the education of students who may not always be physically present at a school. [1] [2] Traditionally, this usually involved correspondence courses wherein the student corresponded with the school via post.

Use the Course Planning Worksheet and the Timeline and Task List as planning tools as you map out these four steps for developing a quality online course: Step 1: Determine learning objectives for each unit/topic of instruction.

Introduction to Distance Learning Distance learning traditionally has provided access to instructional programs for students who are separated by time and/or physical location from an instructor. Distance learning has been thought of as prepackaged text, audio, and/or video courses taken by an isolated learner with limited interaction with an instructor or other students.

Distance education or long-distance learning is the education of students who may not always be physically present at a school.

[1] [2] Traditionally, this usually involved correspondence courses wherein the student corresponded with the school via post. An Introduction to Distance Education is a comprehensive look at the field today, outlining current theories, practices and goals.

Distance education or long-distance learning is the education of students who may not always be physically present at a school. [1] [2] Traditionally, this usually involved correspondence courses wherein the student corresponded with the school via post. Distance learning applications in higher education and industry are growing at a rapid pace. It is now possible to obtain a college degree without physically attending a traditional class. Likewise, numerous companies are using distance learning technologies to distribute training . An Introduction To Distance Learning. Print Reference this. Disclaimer: This work has been submitted by a student. This is not an example of the work written by our professional academic writers. You can view samples of our professional work here.

The book reviews the influence of past distance education theory and practice, along with current changes.

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An Introduction to e-Learning