Data-Driven Sizing Specification Utilizing Consumer Text Reviews

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Rushtin Chaklader ◽  
Matthew B. Parkinson
Keyword(s):  
Data Mining ◽  
Observational Studies ◽  
New Method ◽  
Preliminary Design ◽  
Specific Product ◽  
Consumer Reviews ◽  
Design Specifications ◽  
Product Features ◽  
Large Numbers ◽  
Automated Tool

The objective of this work is to introduce a new method for determining preliminary design specifications related to human-artifact interaction. This new method uses data mining of large numbers of consumer reviews. User opinion on specific product features can be time-consuming or expensive to obtain through traditional methods including surveys, experiments, and observational studies. Data mining review text of already released products may be a potentially less time consuming and costly method. Previously established methods of determining design for human variability information from consumer reviews, such as the frequency and accuracy summation (FAS) number and subsequent manual analysis, are explored. The weighted phrase rating (WPR), a new metric which can be an automated tool to quickly analyze consumer reviews, is also introduced. It does not require manual parsing of the reviews, which extends its applicability to larger review pools. This new method is shown to quickly and economically provide information useful to the establishment of design specifications.

The Garment Fashionable Shape Prognosticating Based on Manufacture Database

2011 ◽  
Vol 55-57 ◽  
pp. 1091-1096
Author(s):  
Xiao Gang Wang ◽  
Xin Zhan Li ◽  
Yue Li
Keyword(s):  
Data Mining ◽  
Body Size ◽  
Female Body ◽  
Science Research ◽  
New Method ◽  
Mining Technology ◽  
Cad System ◽  
Large Numbers ◽  
Error Bar ◽  
Historical Database

Based on the research about outside shape of woman warm jacket more than twenty years, fashion variables that were representative and can describe the fashionable shape were discussed. Experiment was designed to achieve data of large numbers of female body. Body size variables were statistically analyzed to decide the module that was the basement for achieving data from historical photos. Fashionable characteristic diagrams of garment length, front chest width, shoulder length, collar height and their error bar charts were drawn for discussing the change of fashionable shape. The fashion trends in the future were also prognosticated scientifically. At the same time, a historical database was developed for manufacture and designing, which it is the basement for automatic pattern designing. This new method for fashion trend research was introduced by data mining technology, which it opens our minds for garment science research and offers a new database for improving garment CAD system.

Selective Sampling and Orientation of Non-Homogeneous Tissues

1968 ◽  
Vol 26 ◽  
pp. 98-99
Author(s):  
C. C. Clawson ◽  
L. W. Anderson ◽  
R. A. Good
Keyword(s):  
Electron Microscopy ◽  
Electron Microscope ◽  
Light Microscopy ◽  
Random Sampling ◽  
New Method ◽  
Microscope Examination ◽  
Small Areas ◽  
Selective Sampling ◽  
Large Numbers ◽  
Specific Orientation

Investigations which require electron microscope examination of a few specific areas of non-homogeneous tissues make random sampling of small blocks an inefficient and unrewarding procedure. Therefore, several investigators have devised methods which allow obtaining sample blocks for electron microscopy from region of tissue previously identified by light microscopy of present here techniques which make possible: 1) sampling tissue for electron microscopy from selected areas previously identified by light microscopy of relatively large pieces of tissue; 2) dehydration and embedding large numbers of individually identified blocks while keeping each one separate; 3) a new method of maintaining specific orientation of blocks during embedding; 4) special light microscopic staining or fluorescent procedures and electron microscopy on immediately adjacent small areas of tissue.

scholarly journals Characterizing Highly Benefited Patients in Randomized Clinical Trials

2017 ◽  
Vol 13 (1) ◽  
Author(s):  
Vivek Charu ◽  
Paul B. Rosenberg ◽  
Lon S. Schneider ◽  
Lea T. Drye ◽  
Lisa Rein ◽  
...  
Keyword(s):  
Randomized Clinical Trials ◽  
Controlled Trial ◽  
New Method ◽  
Usual Model ◽  
Randomized Controlled ◽  
Working Model ◽  
Large Numbers ◽  
High Benefit ◽  
Two Stages

AbstractPhysicians and patients may choose a certain treatment only if it is predicted to have a large effect for the profile of that patient. We consider randomized controlled trials in which the clinical goal is to identify as many patients as possible that can highly benefit from the treatment. This is challenging with large numbers of covariate profiles, first, because the theoretical, exact method is not feasible, and, second, because usual model-based methods typically give incorrect results. Better, more recent methods use a two-stage approach, where a first stage estimates a working model to produce a scalar predictor of the treatment effect for each covariate profile; and a second stage estimates empirically a high-benefit group based on the first-stage predictor. The problem with these methods is that each of the two stages is usually agnostic about the role of the other one in addressing the clinical goal. We propose a method that characterizes highly benefited patients by linking model estimation directly to the particular clinical goal. It is shown that the new method has the following two key properties in comparison with existing approaches: first, the meaning of the solution with regard to the clinical goal is the same, and second, the value of the solution is the best that can be achieved when using the working model as a predictor, even if that model is incorrect. In the Citalopram for Agitation in Alzheimer’s Disease (CitAD) randomized controlled trial, the new method identifies substantially larger groups of highly benefited patients, many of whom are missed by the standard method.

scholarly journals Hybrid Method for Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining

2021 ◽  
Vol 09 (04) ◽  
Author(s):  
Dr. S. Thavamani ◽  
Keyword(s):  
Data Mining ◽  
Image Retrieval ◽  
Hybrid Method ◽  
Video Retrieval ◽  
New Method ◽  
Computational Time ◽  
Storage Space ◽  
Copy Detection ◽  
Retrieval Technique ◽  
Improved Accuracy

Duplicated images cause several problems in online sites, so these demand special attention. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. We use the new method of eliminating duplicates in this example. To address the disadvantages of frames copy detection, the Hybrid Method of Detecting Duplicate Image by Using Image Retrieval Technique in Data Mining was proposed. The new method of eliminating duplicates in this example has proposed. Using this method, you can get rid of frames that aren't relevant to the video. This makes for more precise and faster video retrieval with fewer duplicates. As a back end, this technique is implemented in C# and SQL. The findings are put to the test and compared to the current SIFT process. The results showed that the output improved accuracy while reducing storage space, computational time, and memory use.

AN INFORMATION-THEORETIC FILTER METHOD FOR FEATURE WEIGHTING IN NAIVE BAYES

2014 ◽  
Vol 28 (05) ◽  
pp. 1451007 ◽  
Author(s):  
CHANG-HWAN LEE
Keyword(s):  
Data Mining ◽  
Bayesian Learning ◽  
State Of The Art ◽  
Feature Weighting ◽  
New Method ◽  
Filter Method ◽  
Information Theoretic ◽  
Naive Bayesian ◽  
Naïve Bayesian ◽  
Unrealistic Assumption

In spite of its simplicity, naive Bayesian learning has been widely used in many data mining applications. However, the unrealistic assumption that all features are equally important negatively impacts the performance of naive Bayesian learning. In this paper, we propose a new method that uses a Kullback–Leibler measure to calculate the weights of the features analyzed in naive Bayesian learning. Its performance is compared to that of other state-of-the-art methods over a number of datasets.

Using Genetic Algorithms to Set Target Values for Engineering Characteristics in the House of Quality

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Penalty Functions ◽  
Preliminary Design ◽  
Test Results ◽  
House Of Quality ◽  
Design Specifications ◽  
Engineering Characteristics ◽  
Target Values

Abstract As part of a strategy for obtaining preliminary design specifications from the House of Quality, genetic algorithms were used to generate and optimize preliminary design specifications for an automotive case study. This paper describes the House of Quality for the automotive case study. In addition, the genetic algorithm chosen, the genetic coding, the methods used for mutation and reproduction, and the fitness and penalty functions are descrobed. Methods for determining convergence are examined. Finally, test results show that the genetic algorithm produces reasonable preliminary design specifications.

A Strategy for Extending the House of Quality to Obtain Preliminary Design Specifications

1999 ◽  
Author(s):  
Patricia Brackin ◽  
Jonathan Colton
Keyword(s):  
Genetic Algorithms ◽  
Automotive Industry ◽  
Test Case ◽  
Preliminary Design ◽  
House Of Quality ◽  
Design Specifications

Abstract This paper details a strategy for quantifying the House of Quality to produce preliminary design specifications. Estimation modules replace the traditional symbols in the HoQ. Genetic algorithms are used to produce and rate candidate preliminary design specifications. A test case from the automotive industry is implemented in order to test the effectiveness of the strategy. Results from the test case indicate that the strategy produces preliminary design specifications that are reasonable and consistent.

A Novel Efficient Mining Association Rules Algorithm for Distributed Databases

2010 ◽  
Vol 108-111 ◽  
pp. 50-56 ◽  
Author(s):  
Liang Zhong Shen
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
New Method ◽  
Rule Mining ◽  
Mining Association Rules

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate professionals, association rule mining is receiving increasing attention. The technology of data mining is applied in analyzing data in databases. This paper puts forward a new method which is suit to design the distributed databases.

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