scholarly journals Using Customer Characteristics to Manage Marketing and Revenue Management Activities

TEM Journal ◽  
2020 ◽  
pp. 1088-1093
Author(s):  
Stepan Chalupa ◽  
Martin Petricek
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Length Of Stay ◽  
Revenue Management ◽  
Statistical Approach ◽  
Valuable Insight ◽  
Data Mining Techniques ◽  
Customer Behaviour ◽  
Type Rate

Understanding customer behaviour is an essential activity for hotel marketers and revenue managers. This article presents the statistical approach based on the data mining techniques focused on the extraction of valuable insight from big data. Using Two-Step Clustering, four major customers segments were identified, including their characteristics. Their description based on the booked room type, rate plan, booking window, net average room rate and length of stay can help the manager to plan better their activities.

scholarly journals Use of Data Mining Techniques to Determine and Predict Length of Stay of Cardiac Patients

2013 ◽  
Vol 19 (2) ◽  
pp. 121 ◽  
Author(s):  
Peyman Rezaei Hachesu ◽  
Maryam Ahmadi ◽  
Somayyeh Alizadeh ◽  
Farahnaz Sadoughi
Keyword(s):  
Data Mining ◽  
Length Of Stay ◽  
Cardiac Patients ◽  
Data Mining Techniques ◽  
Use Of Data

Cardiovascular Disease Prediction Using Data Mining Techniques

2019 ◽  
pp. 246-264
Author(s):  
Kalyani Kadam ◽  
Pooja Vinayak Kamat ◽  
Amita P. Malav
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Big Data ◽  
Cardiovascular Diseases ◽  
Primary Objective ◽  
Disease Prediction ◽  
Data Mining Techniques ◽  
Data Architecture ◽  
Life Threatening ◽  
Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

A Review of Different Data Mining Techniques Used in Big Data Applications

2021 ◽  
pp. 59-89
Author(s):  
Chandrakanta Mahanty ◽  
Devpriya Panda ◽  
Brojo Kishore Mishra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Mining Techniques ◽  
Big Data Applications

Mining Big Data and Streams

2018 ◽  
pp. 406-417
Author(s):  
Hoda Ahmed Abdelhafez
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Large Datasets ◽  
Lessons Learned ◽  
Velocity Data ◽  
Time Data ◽  
Data Mining Techniques ◽  
Complex Information ◽  
Very Large Datasets ◽  
Data Volume

Mining big data is getting a lot of attention currently because the businesses need more complex information in order to increase their revenue and gain competitive advantage. Therefore, mining the huge amount of data as well as mining real-time data needs to be done by new data mining techniques/approaches. This chapter will discuss big data volume, variety and velocity, data mining techniques and open source tools for handling very large datasets. Moreover, the chapter will focus on two industrial areas telecommunications and healthcare and lessons learned from them.

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