Biometric System and Data Analysis: Design, Evaluation, and Data Mining20092Ted Dunstone and Neil Yager. Biometric System and Data Analysis: Design, Evaluation, and Data Mining. New York, NY: Springer 2008. 268 pp., ISBN: 978‐0‐387‐77625‐5 (contact www.springer.com for current price)

Kybernetes ◽  
2009 ◽  
Vol 38 (9) ◽  
pp. 1637-1637 ◽  
Author(s):  
W.R. Howard
Keyword(s):  
Data Mining ◽  
New York ◽  
Data Analysis ◽  
Design Evaluation ◽  
Biometric System ◽  
Analysis Design ◽  
Current Price

scholarly journals PENGARUH MANAJEMEN HUBUNGAN PELANGGAN TERHADAP KEPERCAYAAN DAN KOMITMEN PADA SALON NEW YORK 1 MATARAM

2015 ◽  
Vol 4 (3) ◽  
Author(s):  
Nendy Pratama Agusfianto
Keyword(s):  
New York ◽  
Data Analysis ◽  
Path Analysis ◽  
Research Data ◽  
Consumer Trust ◽  
Beauty Salon ◽  
Customer Relation Management ◽  
Analysis Design ◽  
Opinion Model ◽  
Positive Effect

This study aims to (1) reveal the effect of customer relation management oncustomers’ commitments, (2) to find out the effect of customer relation management andcustomers’ trust, and (3) to uncover the effect of consumer trust on their commitment. Thistype of study belongs to associative research, which aims to investigate the relationshipbetween two or more variables. The population in this study encompassed all the customers ofNew York 1 beauty salon in Mataram. Using Malhotra opinion model, the number of samplesobtained was 65 people. The research data were collected through of use a questionnairedistributed to the a number of respondents, and the data analysis design employed for thepurpose was path analysis. Results of the analysis based on path analysis were presented asfollows (1) the management of customer relation management had no significant effect on thecustomers’ commitments, (2) the customer relation management had a significant and positiveimpact on the customers’ confidence, (3) the trust had a significant and positive effect on thecustomers’ commitment.Keywords : Customer Relation Management, Confidence, Commitment

K-MEANS CLUSTERING ALGORITHM FOR SERVICE DATA ANALYSIS BASED ON CUSTOMERS COMBINATION

2018 ◽  
Vol 3 (1) ◽  
pp. 001
Author(s):  
Zulhendra Zulhendra ◽  
Gunadi Widi Nurcahyo ◽  
Julius Santony
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Clustering Algorithm ◽  
Customer Complaints ◽  
Using Data ◽  
Clustering Data ◽  
Service Data ◽  
Selection Of

In this study using Data Mining, namely K-Means Clustering. Data Mining can be used in searching for a large enough data analysis that aims to enable Indocomputer to know and classify service data based on customer complaints using Weka Software. In this study using the algorithm K-Means Clustering to predict or classify complaints about hardware damage on Payakumbuh Indocomputer. And can find out the data of Laptop brands most do service on Indocomputer Payakumbuh as one of the recommendations to consumers for the selection of Laptops.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

2021 ◽  
Author(s):  
Scott A. Jarmusch ◽  
Justin J. J. van der Hooft ◽  
Pieter C. Dorrestein ◽  
Alan K. Jarmusch
Keyword(s):  
Mass Spectrometry ◽  
Data Mining ◽  
Natural Products ◽  
Data Analysis ◽  
Community Participation ◽  
Mass Spectrometry Data ◽  
Metabolomics Data ◽  
Analysis Tools ◽  
Public Data ◽  
Potential Use

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Creating the Scratch Cooked School Food Framework: Qualitative Data Analysis of a Scratch Cooking Pilot

2021 ◽  
pp. 152483992110389
Author(s):  
Deeana Ijaz Ahmed ◽  
Raynika Trent ◽  
Pamela Koch
Keyword(s):  
New York ◽  
Data Analysis ◽  
Food Environment ◽  
Food System ◽  
System Structure ◽  
Structural Elements ◽  
School Food ◽  
Intervention Level ◽  
Key Stakeholders ◽  
School Food Environment

The purpose of this study is to develop a novel framework that outlines the system required to implement scratch cooking in school kitchens. The data used in this study were 57 interviews with key stakeholders during the Return to Scratch Cooking Pilot that occurred in two New York City school kitchens in 2018–2019 and made significant modifications to many aspects of the existing school food system. The guiding framework for the data analysis was Meadows’s Intervention Level Framework. Intervention Level Framework describes analyzing systems by examining five layers: (1) paradigm shift, (2) goals, (3) system structure (4) feedback and delays, and (5) structural elements. It also provides a framework for describing a system by defining its elements, interconnections, and purpose. Data analysis revealed four elements of the school food system: ingredients and recipes, kitchen, cooking, and the community. The interconnections that played a role in each of these elements were policies, practice, people, and promotion. Together, these four elements and four interconnections comprise the Scratch Cooked School Food framework, which has the purpose of being a tool for researchers and practitioners to utilize when planning, implementing, and evaluating scratch cooking in the school food environment.

Sign in / Sign up

Export Citation Format

Share Document