Energy Characterization of Data Mining Algorithms on Mobile Devices

2013 ◽  
pp. 98-113
Author(s):  
Carmela Comito ◽  
Domenico Talia
Keyword(s):  
Data Mining ◽  
Mobile Devices ◽  
Data Mining Algorithms ◽  
Mining Algorithms

A Two-Stage Zone Regression Method for Global Characterization of a Project Database

2009 ◽  
pp. 2000-2009
Author(s):  
J. J. Dolado ◽  
D. Rodríguez ◽  
J. Riquelme ◽  
F. Ferrer-Troyano ◽  
J. J. Cuadrado
Keyword(s):  
Data Mining ◽  
Project Managers ◽  
Regression Method ◽  
Lower Number ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Global Vision ◽  
Mining Algorithms ◽  
Prediction Capability

One of the problems found in generic project databases, where the data is collected from different organizations, is the large disparity of its instances. In this chapter, we characterize the database selecting both attributes and instances so that project managers can have a better global vision of the data they manage. To achieve that, we first make use of data mining algorithms to create clusters. From each cluster, instances are selected to obtain a final subset of the database. The result of the process is a smaller database which maintains the prediction capability and has a lower number of instances and attributes than the original, yet allow us to produce better predictions.

A characterization of data mining algorithms on a modern processor

2005 ◽  
Author(s):  
Amol Ghoting ◽  
Gregory Buehrer ◽  
Srinivasan Parthasarathy ◽  
Daehyun Kim ◽  
Anthony Nguyen ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Mining Algorithms ◽  
Mining Algorithms

A Two-Stage Zone Regression Method for Global Characterization of a Project Database

2011 ◽  
pp. 1-13 ◽  
Author(s):  
J. J. Dolado ◽  
D. Rodríguez ◽  
J. Riquelme ◽  
F. Ferrer-Troyano ◽  
J. J. Cuadrado
Keyword(s):  
Data Mining ◽  
Project Managers ◽  
Regression Method ◽  
Lower Number ◽  
Data Mining Algorithms ◽  
Use Of Data ◽  
Global Vision ◽  
Mining Algorithms ◽  
Prediction Capability

One of the problems found in generic project databases, where the data is collected from different organizations, is the large disparity of its instances. In this chapter, we characterize the database selecting both attributes and instances so that project managers can have a better global vision of the data they manage. To achieve that, we first make use of data mining algorithms to create clusters. From each cluster, instances are selected to obtain a final subset of the database. The result of the process is a smaller database which maintains the prediction capability and has a lower number of instances and attributes than the original, yet allow us to produce better predictions.

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
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