Application of Data Mining Tools for Long-Term Quantitative and Qualitative Prediction of Streamflow

Marketing concept has progressed through different phases of evolution in the past. At the moment, customer relationship management is considered as the last era of marketing development. The main purpose of this approach is to build long-term oriented profitable relationships with customers. So, companies should know better their customers. This knowledge can be created through a deeper analysis of companies' data with data mining tools. Companies which are able to use data mining tools will gain strong competitive advantages for their strategic decisions. Hotel industry is selected in this study, since it provides a warehouse of customer comments from which precious knowledge can be obtained if text mining as a data mining tool is used appropriately. Thus, this study attempts to explain the stages of text mining with the use of Rapidminer. As a result, different approaches according to the customer satisfaction/dissatisfaction are discussed to build competitive advantages.

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Revealing the Origin and Nature of Drug Resistance of Dynamic Tumour Systems

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch020 ◽

2013 ◽

pp. 354-382

Author(s):

Ricardo Santiago-Mozos ◽

Imtiaz A. Khan ◽

Michael G. Madden

Keyword(s):

Data Mining ◽

Cytotoxic Effect ◽

Statistical Significance ◽

Cell Lineage ◽

Clonal Expansion ◽

Interpretable Models ◽

Microscopy Data ◽

Mining Tools ◽

Over Time

In this paper, the authors identify the strategies that resistant subpopulations of cancer cells undertake to overcome the effect of the anticancer drug Topotecan. For the analyses of cell lineage data encoded from timelapse microscopy, data mining tools are chosen that generate interpretable models of the data, addressing their statistical significance. By interpreting the short-term and long-term cytotoxic effect of Topotecan through these data models, the authors reveal the strategies that resistant subpopulations of cells undertake to maximize their clonal expansion potential. In this context, this paper identifies a pattern of cell death independent of cytotoxic effect. Finally, it is observed that cells exposed to Topotecan have higher movement over time, indicating a putative relationship between cytotoxic effect and cell motility.

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The economics of selection of mail orders Drs. Zahavi and Levin are the masterminds behind the development of AMOS, a customized predictive modeling system for the Franklin Mint in Philadelphia, and GainSmarts, a general purpose data mining system that is the two-time winner of the KDD-CUP competition for the best data mining tools (1997 and 1998) sponsored by the American Association for Artificial Intelligence.

Journal of Interactive Marketing ◽

10.1002/dir.1016.abs ◽

2001 ◽

Vol 15 (3) ◽

pp. 53

Author(s):

Nissan Levin ◽

Jacob Zahavi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Predictive Modeling ◽

American Association ◽

General Purpose ◽

Mining System ◽

Data Mining System ◽

Mining Tools ◽

Selection Of

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Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

Journal of Transportation Safety & Security ◽

10.1080/19439962.2021.1898070 ◽

2021 ◽

pp. 1-19

Author(s):

Bahar Dadashova ◽

Chiara Silvestri-Dobrovolny ◽

Jayveersinh Chauhan ◽

Marcie Perez ◽

Roger Bligh

Keyword(s):

Data Mining ◽

Hot Spot ◽

Multinomial Logit ◽

Hot Spot Analysis ◽

Mining Tools ◽

Spot Analysis

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Rationale for Timing of Follow-Up Visits to Assess Gluten-Free Diet in Celiac Disease Patients Based on Data Mining

Nutrients ◽

10.3390/nu13020357 ◽

2021 ◽

Vol 13 (2) ◽

pp. 357

Author(s):

Alfonso Rodríguez-Herrera ◽

Joaquín Reyes-Andrade ◽

Cristina Rubio-Escudero

Keyword(s):

Data Mining ◽

Celiac Disease ◽

Cultural Context ◽

Gluten Free Diet ◽

Gluten Free ◽

Adherence To Diet ◽

And Gender ◽

Few Data

The assessment of compliance of gluten-free diet (GFD) is a keystone in the supervision of celiac disease (CD) patients. Few data are available documenting evidence-based follow-up frequency for CD patients. In this work we aim at creating a criterion for timing of clinical follow-up for CD patients using data mining. We have applied data mining to a dataset with 188 CD patients on GFD (75% of them are children below 14 years old), evaluating the presence of gluten immunogenic peptides (GIP) in stools as an adherence to diet marker. The variables considered are gender, age, years following GFD and adherence to the GFD by fecal GIP. The results identify patients on GFD for more than two years (41.5% of the patients) as more prone to poor compliance and so needing more frequent follow-up than patients with less than 2 years on GFD. This is against the usual clinical practice of following less patients on long term GFD, as they are supposed to perform better. Our results support different timing follow-up frequency taking into consideration the number of years on GFD, age and gender. Patients on long term GFD should have a more frequent monitoring as they show a higher level of gluten exposure. A gender perspective should also be considered as non-compliance is partially linked to gender in our results: Males tend to get more gluten exposure, at least in the cultural context where our study was carried out. Children tend to perform better than teenagers or adults.

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Estimation of long-term paediatric within-individual biological variation using a data mining approach

Clinical Biochemistry ◽

10.1016/j.clinbiochem.2014.04.058 ◽

2014 ◽

Vol 47 (9) ◽

pp. 775

Author(s):

T.P. Loh ◽

E. Ranieri ◽

M. Metz

Keyword(s):

Data Mining ◽

Biological Variation ◽

Data Mining Approach

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High-Resolution Mass Spectrometry Associated with Data Mining Tools for the Detection of Pollutants and Chemical Characterization of Honey Samples

Journal of Agricultural and Food Chemistry ◽

10.1021/jf504400c ◽

2014 ◽

Vol 62 (46) ◽

pp. 11335-11345 ◽

Cited By ~ 37

Author(s):

Jérôme Cotton ◽

Fanny Leroux ◽

Simon Broudin ◽

Mylène Marie ◽

Bruno Corman ◽

...

Keyword(s):

Mass Spectrometry ◽

Data Mining ◽

High Resolution ◽

High Resolution Mass Spectrometry ◽

Chemical Characterization ◽

High Resolution Mass ◽

Mining Tools ◽

Resolution Mass

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DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

International Journal of Software Engineering and Knowledge Engineering ◽

10.1142/s0218194004001841 ◽

2004 ◽

Vol 14 (06) ◽

pp. 665-695 ◽

Cited By ~ 6

Author(s):

J. L. ÁLVAREZ-MACÍAS ◽

J. MATA-VÁZQUEZ ◽

J. C. RIQUELME-SANTOS

Keyword(s):

Data Mining ◽

Software Development ◽

Unsupervised Learning ◽

Supervised Learning ◽

Development Process ◽

A Priori ◽

Post Mortem ◽

Software Development Process ◽

A Priori Analysis ◽

Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

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