Abstract
In order to generate precise behavioural patterns or user segmentation, organisations often struggle with pulling information from data and choosing suitable Machine Learning (ML) techniques. Furthermore, many marketing teams are unfamiliar with data-driven classification methods. The goal of this research is to provide a framework that outlines the Unsupervised Machine Learning (UML) methods for User-Profiling (UP) based on essential data attributes. A thorough literature study was undertaken on the most popular UML techniques and their dataset attributes needs. For UP, a structure is developed that outlines several UML techniques. In terms of data size and dimensions, it offers two-stage clustering algorithms for category, quantitative, and mixed types of datasets. The clusters are determined in the first step using a multilevel or model-based classification method. Cluster refining is done in the second step using a non-hierarchical clustering technique. Academics and professionals may use the framework to figure out which UML techniques are best for creating strong profiles or data-driven user segmentation.
Highlights
The Internet of Things (IoT), Neurology, Machine Intelligence, and Data Gathering have fuelled the thirst for data for rational decision and personalisation
Academics and professionals may use the framework to figure out which Unsupervised Machine Learning (UML) techniques are best for creating strong profiles or data-driven user segmentation
Machine Learning (ML) [1] is "the study of computer techniques to systematize the process of information accumulation from instances." It is classified into two types: supervised and Unsupervised Machine Learning (UML)
Summary
The Internet of Things (IoT), Neurology, Machine Intelligence, and Data Gathering have fuelled the thirst for data for rational decision and personalisation. The availability of vast volumes of datasets for the aims of dividing the client base, delivering personalised service, as well as collecting valuable knowledge offered by diverse data sources is a significant competitive edge for modern organisations. Deep learning is used to actionable insights from unstructured information. Machine Learning (ML) [1] is "the study of computer techniques to systematize the process of information accumulation from instances." It is classified into two types: supervised and Unsupervised Machine Learning (UML). There is no variable of goals in UML, and the input datasets are just supplied. This research concentrates on the application of UML for segment customers and behavior analysis based on data. A user profile often comprises data such as spatial, psycho - graphic, or behavioural data
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