Chapter2 Digital Components Dr. Bernard Chen Ph.D. University of Central Arkansas Spring 2009.
Clustering and Research Works Dr. Bernard Chen Ph.D. University of Central Arkansas.
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Transcript of Clustering and Research Works Dr. Bernard Chen Ph.D. University of Central Arkansas.
Clustering and Research Works
Dr. Bernard Chen Ph.D.University of Central Arkansas
Outline
Clustering Data Science Future Works
Clustering Algorithms
There are two clustering algorithms we used in our approach:
K-means Clustering Fuzzy C-means Clustering
K-means Clustering
K-means Clustering
K-means Clustering
K-means Clustering
K-means Clustering
Fuzzy C-means Clustering
Fuzzy C-means Clustering
Fuzzy C-means Clustering
Fuzzy C-means Clustering
Fuzzy C-means Clustering
Fuzzy C-means Clustering
Fuzzy C-means Clustering
Real World example
Outline
Clustering Data Science Future Works
Data Science wikipedia
Data science is the study of the generalizable extraction of knowledge from data.
It incorporates varying elements and builds on techniques and theories from many fields
wikipedia
Outline
Clustering Data Science Future Works
Data Science wikipedia
A practitioner of data science is called a data scientist.
Data scientists solve complex data problems through employing deep expertise in some scientific discipline.
It is generally expected that data scientists are able to work with various elements
Data Science wikipedia
Good data scientists are able to apply their skills to achieve a broad spectrum of end results.
the ability to find and interpret rich data sources, manage large amounts of data despite hardware,
software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data, build mathematical models using the data, present and communicate the data insights/findings
to specialists and scientists in their team and if required to a naive audience.
Outline
Clustering Data Science Future Works
Data Science in WINE
Once viewed as a luxury good, nowadays wine is increasingly enjoyed by a wider range of consumers.
Wine certification is generally assessed by physicochemical and sensory tests
sensory tests Example: Chateau Latour 2010
http://www.wine.com/V6/Chateau-Latour-2010/wine/110508/detail.aspx
sensory tests Among those expert reviews, we use “Wine
Spectator’s” version
"Unbelievably pure, with distilled cassis and plum fruit that cuts a very precise path, while embers of anise, violet and black cherry configure form a gorgeous backdrop. A bedrock of graphite structure should help this outlive other 2010s. Powerful, sleek and incredibly long. Not perfect, but very close. Best from 2020 through 2050."99 Points Wine Spectator
sensory tests
Wine Spectator has the following advantages: Words are precise Well-known Famous for it’s Top 100 wine of the
year selection Well maintained database
Research Topic 1
Clustering on past 10 years Top 100 wine (1000 wines)
Challenges: Extract attributes from 1000 wine Clustering algorithm Analysis of the results
Research Topic 2 Multi-label (4 classes) Classification on
1000 wines, which composed of 250 wines for 4 category (95+, 90~94, 89~85, 85-)
Challenges: Classification algorithm 4 classes How to improve accuracy
Research Topic 3 Association Rules on region-specific
dataset (such as Napa) for attribute correlation and quality prediction.
Challenges: Association Rules algorithm Analysis of the results How to improve accuracy
Research Topic 4 Region Prediction (such as France vs
Italy), open for association rules or classification algorithms.
Challenges: More free-style (more suitable for
experienced researchers) Not only focus on accuracy, but also try to
tell the difference between the regions
Research Topic 5
Clustering + Classification for higher accuracy prediction.
Challenges: TWO type of algorithms More complex in understanding and
coding
Research Topic 6
Multi-label research: since we have multiple reviews available, how to use those information for data science research?
Challenges: Very flexible!!!