Sentiment Analysis of College Reviews using Machine Learning & Data Mining

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

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Statistical and Machine-Learning Data Mining

10.1201/b11508 ◽

2011 ◽

Cited By ~ 12

Author(s):

Bruce Ratner ◽

Stephen Day ◽

Christopher Davies

Keyword(s):

Machine Learning ◽

Data Mining ◽

Learning Data

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A Review on Various Algorithms used in Machine Learning

International Journal of Scientific Research in Computer Science Engineering and Information Technology ◽

10.32628/cseit1952248 ◽

2019 ◽

pp. 915-920

Author(s):

Divya Chaudhary ◽

Er. Richa Vasuja

Keyword(s):

Machine Learning ◽

Data Mining ◽

New Technologies ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning System ◽

Training Set ◽

Learning Data ◽

Do So

In today's scenario all of data is being generated by everyone of us . so it becomes vital for us to handle this data. To do so new technologies are being developed such as machine learning, data mining etc. This paper gives the study related to machine learning(ML).Precise approximations are repetitively being produced by Machine Learning algorithms. Machine learning system effectively “learns” how to guess from training set of completed jobs. The main purpose of the review is to give a jagged estimate or overview about the mostly used algorithms in machine learning.

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Data Mining, Machine Learning, Deep Learning, Chemometrics. Definitions, common points and Trends (Spoiler Alert: VALIDATE your models!)

Brazilian Journal of Analytical Chemistry ◽

10.30744/brjac.2179-3425.ar-38-2021 ◽

2021 ◽

Vol 8 (32) ◽

pp. 22-38

Author(s):

José Manuel Amigo

Keyword(s):

Analytical Chemistry ◽

Artificial Intelligence ◽

Machine Learning ◽

Data Mining ◽

Deep Learning ◽

Daily Life ◽

Big Data Analysis ◽

Mining Machine ◽

Learning Data ◽

Made In

Concepts like Machine Learning, Data Mining or Artificial Intelligence have become part of our daily life. This is mostly due to the incredible advances made in computation (hardware and software), the increasing capabilities of generating and storing all types of data and, especially, the benefits (societal and economical) that generate the analysis of such data. Simultaneously, Chemometrics has played an important role since the late 1970s, analyzing data within natural science (and especially in Analytical Chemistry). Even with the strong parallelisms between all of the abovementioned terms and being popular with most of us, it is still difficult to clearly define or differentiate the meaning of Machine Learning, Data Mining, Artificial Intelligence, Deep Learning and Chemometrics. This manuscript brings some light to the definitions of Machine Learning, Data Mining, Artificial Intelligence and Big Data Analysis, defines their application ranges and seeks an application space within the field of analytical chemistry (a.k.a. Chemometrics). The manuscript is full of personal, sometimes probably subjective, opinions and statements. Therefore, all opinions here are open for constructive discussion with the only purpose of Learning (like the Machines do nowadays).

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Forensics: Assessing model goodness: A machine learning view

Encyclopedia with Semantic Computing and Robotic Intelligence ◽

10.1142/s2529737618500156 ◽

2018 ◽

Vol 02 (02) ◽

pp. 1850015 ◽

Cited By ~ 1

Author(s):

Joseph R. Barr ◽

Joseph Cavanaugh

Keyword(s):

Machine Learning ◽

Data Mining ◽

Statistical Model ◽

Statistical Learning ◽

Statistical Models ◽

The Subject ◽

The Many ◽

The One ◽

Compare And Contrast ◽

Learning Data

It is not unusual that efforts to validate a statistical model exceed those used to build the model. Multiple techniques are used to validate, compare and contrast among competing statistical models: Some are concerned with a model’s ability to predict new data while others are concerned with model descriptiveness of the data. Without claiming to provide a comprehensive view of the landscape, in this paper we will touch on both aspects of model validation. There is much more to the subject and the reader is referred to any of the many classical statistical texts including the revised two volumes of Bickel and Docksum (2016), the one by Hastie, Tibshirani, and Friedman [The Elements of Statistical Learning: Data Mining, Inference, and Predication, 2nd edn. (Springer, 2009)], and several others listed in the bibliography.

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Simultaneous Feature Selection and Tuple Selection for Efficient Classification

Complex Data Warehousing and Knowledge Discovery for Advanced Retrieval Development ◽

10.4018/978-1-60566-748-5.ch012 ◽

2010 ◽

pp. 270-285

Author(s):

Manoranjan Dash ◽

Vivekanand Gopalkrishnan

Keyword(s):

Gene Expression ◽

Machine Learning ◽

Data Mining ◽

Feature Selection ◽

Distance Measure ◽

Microarray Gene Expression ◽

Research Areas ◽

Microarray Gene ◽

Selection For ◽

Learning Data

Feature selection and tuple selection help the classifier to focus to achieve similar (or even better) accuracy as compared to the classification without feature selection and tuple selection. Although feature selection and tuple selection have been studied earlier in various research areas such as machine learning, data mining, and so on, they have rarely been studied together. The contribution of this chapter is that the authors propose a novel distance measure to select the most representative features and tuples. Their experiments are conducted over some microarray gene expression datasets, UCI machine learning and KDD datasets. Results show that the proposed method outperforms the existing methods quite significantly.

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Interactive Classification Using a Granule Network

Novel Approaches in Cognitive Informatics and Natural Intelligence ◽

10.4018/978-1-60566-170-4.ch016 ◽

2011 ◽

pp. 235-245

Author(s):

Yan Zhao ◽

Yiyu Yao

Keyword(s):

Machine Learning ◽

Data Mining ◽

Pattern Recognition ◽

Classification System ◽

Search Space ◽

Classification Methods ◽

Classification Problems ◽

Computing Model ◽

Tree Construction ◽

Learning Data

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This chapter studies interactive classification at 3 levels. At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing model is suggested for re-examining not only existing classification problems, but also interactive classification problems. At the application level, an interactive classification system (ICS), using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction, and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.

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