DataVis - A Platform for Interactive Data Visual Analytics, Exploration, and Mining

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Bioinformatics data3

16 KB ecoli
4 KB iris
57 KB yeast

Educational data4

68 KB student-mat
91 KB student-por
416 KB turkiye-student-evaluation_R_Specific
377 KB turkiye-student-evaluation_generic

Healthcare data17

23 KB Indian-liver-patients
23 KB ThoraricSurgery
5 KB acute-inflammations
399 KB arrhythmia
11 KB blood-transfusion
14 KB breast-cancer-wisc
26 KB dermatology
6 KB echocardiogram
11 KB heart-disease
16 KB heart
8 KB hepatitis
7 KB liver-disorders_bupa
4 KB lung-cancer
13 KB mammographic_masses
35 KB parkinsons
82 KB thyroid-disease_allbp-test
237 KB thyroid-disease_allbp

Misc. data195

20 MB 3D_spatial_network
9 MB Arabic_Digit-Test
24 MB Arabic_Digit_Train
3 MB CASP
2 MB CNAE-9
1 MB CommViolPredUnnormalizedData
21 MB ConfLongDemo_JSI
2 MB EEG-Eye-State
497 B Faults.col
291 KB Faults
640 KB JapaneseVowels-mld_ae-test
481 KB JapaneseVowels-mld_ae-train
27 B JapaneseVowels-mld_size_ae-test
27 B JapaneseVowels-mld_size_ae-train
87 MB MiniBooNE_PID
489 KB SkillCraft1_Dataset
3 MB Skin_NonSkin
3 KB StoneFlakes
9 MB Test_Arabic_Digit
27 MB Train_Arabic_Digit
187 KB abalone
2 MB adult-test
3 MB adult
57 KB airfoil_self_noise
29 MB amazon-commerce-reviews
41 MB amzn-anon-access-samples-history-2
41 MB amzn-anon-access-samples
10 KB annealing_anneal-test
78 KB annealing_anneal
5 KB audiology.standardized-test
38 KB audiology.standardized
28 KB australian
21 KB auto-mpg
26 KB autos_imports-85
6 KB balance-scale
2 KB balloons
151 KB biodeg
20 KB breast-cancer-wisconsin
122 KB breast-cancer-wisconsin_wdbc
44 KB breast-cancer-wisconsin_wpbc
51 KB car
30 KB cmc
29 KB coil-mld_analysis
12 KB coil-mld_eval
1 MB communities
72 MB covtype
9 KB cpu-performance_machine
32 KB credit-screening_crx
101 KB cylinder-bands_bands
44 KB data_banknote_authentication
3 MB eb
218 KB event-detection_CalIt2
1 KB event-detection_CalIt2.labels
840 KB event-detection_Dodgers
4 KB event-detection_Dodgers.labels
3 KB fertility_Diagnosis
15 KB flags
25 KB forest-fires
96 KB german-numeric
78 KB german
10 KB glass
3 KB haberman
280 B hayes-roth-test
2 KB hayes-roth
18 KB heart-disease_processed.cleveland
10 KB heart-disease_processed.hungarian
4 KB heart-disease_processed.switzerland
7 KB heart-disease_processed.va
11 KB heart-disease_reprocessed.hungarian
410 KB hill-valley_Hill_Valley_with_noise_Testing
407 KB hill-valley_Hill_Valley_with_noise_Training
705 KB hill-valley_Hill_Valley_without_noise_Testing
705 KB hill-valley_Hill_Valley_without_noise_Training
6 KB horse-colic-test
25 KB horse-colic
41 KB housing
337 KB image_segmentation-test
34 KB image_segmentation
10 MB internet_ads_ad
75 KB ionosphere
5 KB iris_bezdekIris
240 KB king-rook-vs-king-pawn_kr-vs-kp
519 KB king-rook-vs-king_krkopt
416 B lenses
696 KB letter-recognition
1 MB magic04
67 KB meta-data
2 MB mfeat-fac
2 MB mfeat-fou
2 MB mfeat-kar
168 KB mfeat-mor
1 MB mfeat-pix
1 MB mfeat-zer
10 KB monks-problems_monks-1-test
3 KB monks-problems_monks-1-train
10 KB monks-problems_monks-2-test
4 KB monks-problems_monks-2-train
10 KB monks-problems_monks-3-test
3 KB monks-problems_monks-3-train
250 KB movement_libras
31 KB movement_libras_1
188 KB movement_libras_10
63 KB movement_libras_5
94 KB movement_libras_8
31 KB movement_libras_9
365 KB mushroom_agaricus-lepiota
1 MB nursery
259 KB optdigits-test
550 KB optdigits-train
800 KB ozone_eighthr
800 KB ozone_onehr
170 KB pendigits-test
364 KB pendigits-train
23 KB pima-indians-diabetes
2 MB plants
36 KB plrx
22 MB poker-hand-testing
599 KB poker-hand-training
575 KB poker-hand
184 KB pop_failures
5 KB postoperative-patient-data
4 KB postoperative-patient-data_post-operative
231 KB satimage_sat-test
514 KB satimage_sat
5 MB secom
40 KB secom.labels
9 KB seeds_dataset
360 KB segment
125 KB seismic-bumps
3 MB semeion
147 KB sensor_readings_2
850 KB sensor_readings_24
211 KB sensor_readings_4
3 KB servo
264 B shuttle-landing-control
375 KB shuttle-test
1 MB shuttle-train
5 KB slump_test
27 KB solar-flare
8 KB solar-flare1
86 KB sonar
32 KB soybean-large-test
26 KB soybean-large
322 B space-shuttle_o-ring-erosion-or-blowby
322 B space-shuttle_o-ring-erosion
682 KB spambase
8 KB spect_SPECT-test
4 KB spect_SPECT-train
24 KB spect_SPECTF-test
10 KB spect_SPECTF-train
36 KB sponge
282 KB synthetic_control-mld
2 KB tae
890 KB telemonitoring_parkinsons_updrs
82 KB thyroid-disease_allhyper-test
235 KB thyroid-disease_allhyper
82 KB thyroid-disease_allhypo-test
237 KB thyroid-disease_allhypo
82 KB thyroid-disease_allrep-test
235 KB thyroid-disease_allrep
229 KB thyroid-disease_ann-test
251 KB thyroid-disease_ann-train
82 KB thyroid-disease_dis-test
235 KB thyroid-disease_dis
214 KB thyroid-disease_hypothyroid
5 KB thyroid-disease_new-thyroid
215 KB thyroid-disease_sick-euthyroid
81 KB thyroid-disease_sick-test
234 KB thyroid-disease_sick
754 KB thyroid-disease_thyroid0387
983 KB tic-mld_tic-data2000
667 KB tic-mld_tic-eval2000
8 KB tic-mld_tic-tgts2000
25 KB tic-tac-toe
1 KB trains-transformed
6 KB vehicle_xaa
6 KB vehicle_xab
6 KB vehicle_xac
6 KB vehicle_xad
6 KB vehicle_xae
6 KB vehicle_xaf
6 KB vehicle_xag
6 KB vehicle_xah
6 KB vehicle_xai
18 KB voting-records_house-votes-84
72 KB vowel-context
90 KB water-treatment
2 MB wearable-sensors
2 MB wearable_computing_weight_lifting_exer
15 KB wholesale-customers
258 KB wine-quality
11 KB wine
292 B yacht_hydrodynamics.bib
11 KB yacht_hydrodynamics
4 KB zoo

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Create distribution plots by selecting a variable to color (on the left side). For instance, in the iris data, one can color the data points by their species.
Each variable under 'Drill and Expand' (on the right) is annotated with the number of unique values. Categorical variables (as currently defined) are shown in red whereas the number of unique values in other variables are shown in blue.
Semantic zooming (in/out) on the data using the mousewheel (or by pressing '+' and '-').
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Mouseover/click the database icon on the left side to display a list of data sets to interactively explore!
A subset of interesting data points may be selected and their properties may be visualized across all statistics. To select a subset of points, hold down the left mouse button while dragging the mouse in any direction until the data points of interest are highlighted. This feature allows users to explore and analyze various subsets of data points and their important interesting statistics and properties to gain insights into the data.
Once a subset of interesting data points are selected, we also allow for the selecting and drilling down on any of the interesting properties or attributes using the left menu below.
Data points may also be colored using any categorical variable.
Add/delete any number of variables.
Expand the matrix by drilling-down on the values of a specific variable.
Delete data easily by brushing over any set of points (to select them) and pressing 'D' to remove them from the plots and see the impact (on uni/bi-variate stats, etc.) in real-time.
Drag the correlation coefficient and other graphical elements.
Filter data points by values of a categorical variable (i.e., variable used for coloring the data points).
Interactively query the data and filter it using any number of variables. Multiple data filters are easily setup and provide the user with the flexibility to filter the data across N dimensions, simultaneously. For example, one may filter all individuals (data points/rows in the matrix/tensor) with "Age > x_i" and "GPA > 3.0" (as well as any additional constraints on any of the other N variables of interest).
Mouseover data points as well as most graphical elements to see additional information including univariate stats as well as other visual representations.
Create additional categorical variables for coloring, etc. by adjusting a simple threshold
Export and save visualizations in a number of formats including SVG, PDF, as well as PNG, and JPEG. Visualizations can be easily saved for later use, both in your site profile, and to your local computer.
Fit the data using linear regression as well as a non-parametric regression method called loess that combines multiple regression models in a k-nearest-neighbor fashion.
Automatically compute the bandwidth or interactively explore other choices of bandwidth by adjusting a slider.
Number of bins to use for each of the interactive univariate visualizations is determined automatically by default, but one may experiment/explore the impact of other settings by adjusting a slider in real-time.
Select and customize the methods and visual components to include in the visualization (e.g., line of best fit, box and whisker plots, etc.).
Filter outliers quickly by pressing the "remove outliers" button (see the menu on the right).
Represent the data (e.g., scatter plot points) using a number of symbols (e.g., circle, cross, square, triangle). For instance, one may use a symbol to represent the outliers across all plots, and another symbol (e.g., circle) to represent the others.
Scale the size of the visualization elements (or symbols) representing the data using any number of functions including log, sqrt, etc.
Weight the visual representation (symbol) pertaining to each data point using any of the N variables of interest (e.g., GPA, Age, etc.).
To ensure the platform is simple, intuitive and easy-to-use as possible, one may hover over (or mouseover) menus, controls, and other GUI elements to quickly see additional information about each, including the purpose/use-case, description, and other helpful information.

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