scholarly journals An Optimistic Data Mining Approach for Handling Large Data Set using Data Partitioning Techniques

2011 ◽  
Vol 24 (3) ◽  
pp. 29-33 ◽  
Author(s):  
Dipak V. Patil ◽  
R. S. Bichkar
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Data Partitioning ◽  
Data Set ◽  
Data Mining Approach ◽  
Large Data Set ◽  
Using Data

scholarly journals Data mining for energy analysis of a large data set of flats

2017 ◽  
Vol 170 (1) ◽  
pp. 3-18 ◽  
Author(s):  
Alfonso Capozzoli ◽  
Gianluca Serale ◽  
Marco Savino Piscitelli ◽  
Daniele Grassi
Keyword(s):  
Data Mining ◽  
Energy Analysis ◽  
Large Data ◽  
Data Set ◽  
Large Data Set

scholarly journals Evaluating a guest satisfaction model through data mining

2019 ◽  
Vol 32 (4) ◽  
pp. 1523-1538 ◽  
Author(s):  
Sérgio Moro ◽  
Joaquim Esmerado ◽  
Pedro Ramos ◽  
Bráulio Alturas
Keyword(s):  
Data Mining ◽  
Large Data ◽  
Online Reviews ◽  
Theory And Practice ◽  
Target Feature ◽  
Data Set ◽  
Content Type ◽  
Large Data Set ◽  
Guest Satisfaction ◽  
Satisfaction Model

Purpose This paper aims to propose a data mining approach to evaluate a conceptual model in tourism, encompassing a large data set characterized by dimensions grounded on existing literature. Design/methodology/approach The approach is tested using a guest satisfaction model encompassing nine dimensions. A large data set of 84 k online reviews and 31 features was collected from TripAdvisor. The review score granted was considered a proxy of guest satisfaction and was defined as the target feature to model. A sequence of data understanding and preparation tasks led to a tuned set of 60k reviews and 29 input features which were used for training the data mining model. Finally, the data-based sensitivity analysis was adopted to understand which dimensions most influence guest satisfaction. Findings Previous user’s experience with the online platform, individual preferences, and hotel prestige were the most relevant dimensions concerning guests’ satisfaction. On the opposite, homogeneous characteristics among the Las Vegas hotels such as the hotel size were found of little relevance to satisfaction. Originality/value This study intends to set a baseline for an easier adoption of data mining to evaluate conceptual models through a scalable approach, helping to bridge between theory and practice, especially relevant when dealing with Big Data sources such as the social media. Thus, the steps undertaken during the study are detailed to facilitate replication to other models.

Climatology of nutrient distributions in the South China Sea based on a large data set derived from a new algorithm

2021 ◽  
pp. 102586
Author(s):  
Chuanjun Du ◽  
Ruoying He ◽  
Zhiyu Liu ◽  
Tao Huang ◽  
Lifang Wang ◽  
...  
Keyword(s):  
South China Sea ◽  
South China ◽  
Large Data ◽  
The South China Sea ◽  
The South ◽  
Data Set ◽  
China Sea ◽  
Large Data Set

scholarly journals Spike detection: Inter-reader agreement and a statistical Turing test on a large data set

2017 ◽  
Vol 128 (1) ◽  
pp. 243-250 ◽  
Author(s):  
Mark L. Scheuer ◽  
Anto Bagic ◽  
Scott B. Wilson
Keyword(s):  
Large Data ◽  
Turing Test ◽  
Spike Detection ◽  
Data Set ◽  
Large Data Set

Main large data set features detection by a linear predictor model

2014 ◽  
Author(s):  
Carlos Enrique Gutierrez ◽  
Prof. Mohamad Reza Alsharif ◽  
Mahdi Khosravy ◽  
Prof. Katsumi Yamashita ◽  
Prof. Hayao Miyagi ◽  
...  
Keyword(s):  
Large Data ◽  
Data Set ◽  
Linear Predictor ◽  
Large Data Set ◽  
Predictor Model

Governance Problems in Closely Held Corporations

2011 ◽  
Vol 46 (4) ◽  
pp. 943-966 ◽  
Author(s):  
Venky Nagar ◽  
Kathy Petroni ◽  
Daniel Wolfenzon
Keyword(s):  
Ownership Structure ◽  
Large Data ◽  
Minority Shareholders ◽  
Return On Assets ◽  
Data Set ◽  
Large Data Set ◽  
Ownership Rights ◽  
Shared Ownership

AbstractA major governance problem in closely held corporations is the majority shareholders’ expropriation of minority shareholders. As a solution, legal and finance research recommends that the main shareholder surrender some control to minority shareholders via ownership rights. We test this proposition on a large data set of closely held corporations. We find that shared-ownership firms report a substantially larger return on assets and lower expense-to-sales ratios. These findings are robust to institutionally motivated corrections for endogeneity of ownership structure. We provide evidence on the presence of governance problems and the effectiveness of shared ownership as a solution in settings characterized by illiquidity of ownership.

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