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The Internet is a global public network. With the growth of the Internet and its potential, there has been subsequent change in the business model of organizations across the world. More and more people are getting connected to the Internet every day to take advantage of the new business model popularly known as e-Business. Internetwork …
The recent explosion of data set size, in the number of records as well as attributes, has triggered the development of a number of big data platforms as well as parallel data analytics algorithms. At the same time though, it has pushed for the usage of data dimensionality reduction procedures. Dealing with a lot of …
In recommendation systems, there are two main kinds of recommendation systems collaborative and content-based. The collaborative recommendation system is also called a collaborative filter because that reduces the information and identifies the relevant information. The aim of this filter is to focus on user-profiles and try to find similar users. Based on these similar users …
The recommendation system can be developed based on Content-Based filtering, and collaborative filtering models. in this article, we are discussing a content-based Recommendation system. The content-based Recommendation system utilizes the data taken from the user which may be captured directly or indirectly from the user. For example by using a rating, review, or clicking on …
A web recommendation system is an extensive class of Web applications. That involves predicting user responses to options. Such a facility is called a recommendation system. We can take a basic idea of a recommendation system by using two good examples [13]: Online news publishing websites are offering news articles to their readers, based …
An Artificial Neural Network (ANN) is an information processing model. That is inspired by the biological nervous systems like a human brain which process information. It is composed of a number of interconnected processing elements. These processing elements are known as neurons. In order to solve specific problems, for an application. Different ANN structures are …
The fuzzy C Means clustering algorithm is developed to overcome the issue of k-means clustering. The k-means clustering specifies the classes strictly, but Fuzzy C Means clustering can assign more than one class label to an instance. This algorithm works by assigning membership to each data point corresponding to each cluster center on the basis …
k-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number of clusters. The main idea is to define k centers, one for each cluster. These centers should be placed in a cunning …
C4.5 uses “Information gain,” This computation does not, in itself, produce anything new. However, it allows for measuring a gain ratio. The Gain ratio is defined as follows: The C4.5 Algorithm is as follows. Applications of Decision Tree C4.5 In an analysis of coal logistics customer For the analysis of logistic customers need to build …
Clustering is the technique to separate the information. That focuses on the grouping of data that has similar information. Two groups contain different information but have the same information in their own group. Clustering is an unsupervised learning technique that is used to group or categorize similar pattern information. In basic words, the technique to …
