clustering and research works dr. bernard chen ph.d. university of central arkansas
TRANSCRIPT
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Clustering and Research Works
Dr. Bernard Chen Ph.D.University of Central Arkansas
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Outline
Clustering Data Science Future Works
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Clustering Algorithms
There are two clustering algorithms we used in our approach:
K-means Clustering Fuzzy C-means Clustering
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K-means Clustering
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K-means Clustering
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K-means Clustering
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K-means Clustering
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K-means Clustering
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Fuzzy C-means Clustering
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Fuzzy C-means Clustering
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Fuzzy C-means Clustering
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Fuzzy C-means Clustering
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Fuzzy C-means Clustering
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Fuzzy C-means Clustering
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Fuzzy C-means Clustering
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Real World example
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Outline
Clustering Data Science Future Works
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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
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Outline
Clustering Data Science Future Works
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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
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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.
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Outline
Clustering Data Science Future Works
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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
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sensory tests Example: Chateau Latour 2010
http://www.wine.com/V6/Chateau-Latour-2010/wine/110508/detail.aspx
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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
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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
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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
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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
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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
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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
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Research Topic 5
Clustering + Classification for higher accuracy prediction.
Challenges: TWO type of algorithms More complex in understanding and
coding
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Research Topic 6
Multi-label research: since we have multiple reviews available, how to use those information for data science research?
Challenges: Very flexible!!!