Identifying behaviour patterns using machine learning techniques / Tomasz Lelek.

By: Contributor(s): Publisher: [Place of publication not identified] : Packt, [2017]Description: 1 online resource (1 streaming video file (1 hr., 12 min., 18 sec.)) : digital, sound, colorContent type:
  • two-dimensional moving image
Media type:
  • computer
  • video
Carrier type:
  • online resource
Subject(s): Genre/Form: LOC classification:
  • Q325.5
Online resources: Presenter, Tomasz Lelek.Summary: "Nowadays web-sites needs to handle huge amount of traffic. We can leverage that fact and capture user interactions with the application. For further analysis. Next, we can analyze users behavior and capture patterns on which we are able to react properly. In applications that needs to deal with huge amount of traffic it is very hard to detect anomalies. We'll learn how to apply clustering to find anomalies in web traffic. Next, we can analyze users behaviour and when they tend to do on our application using time series data. We will be using GMM clustering technique to achieve that. On the e-commerce sites we want to predict when and what user wants to buy in the future. We can use the Hidden Markov Model to find transitions between states and find the transition with highest probability."--Resource description page.
Item type: eBooks
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Presenter, Tomasz Lelek.

Title from title screen (viewed October 17, 2017).

Date of publication from resource description page.

"Nowadays web-sites needs to handle huge amount of traffic. We can leverage that fact and capture user interactions with the application. For further analysis. Next, we can analyze users behavior and capture patterns on which we are able to react properly. In applications that needs to deal with huge amount of traffic it is very hard to detect anomalies. We'll learn how to apply clustering to find anomalies in web traffic. Next, we can analyze users behaviour and when they tend to do on our application using time series data. We will be using GMM clustering technique to achieve that. On the e-commerce sites we want to predict when and what user wants to buy in the future. We can use the Hidden Markov Model to find transitions between states and find the transition with highest probability."--Resource description page.

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