Since you're using lat / long coords, you'll also want to convert them
to radians and specify 'haversine' as your distance metric; i.e. :
coords = np.vstack([lats.ravel(),longs.ravel()]).T
coords *= np.pi / 180. # to radians
...and:
db = DBSCAN(eps=0.3, min_samples=10, metric='haversine')
# replace eps and min_samples as appropriate
db.fit(coords)
Cheers,
Shane
On 03/30, Sebastian Raschka wrote:
Hi, Shuchi,
1. How can I add data to the data set of the package?
You don’t need to add your dataset to the dataset module to run your analysis.
A convenient way to load it into a numpy array would be via pandas. E.g.,
import pandas as pd
df = pd.read_csv(‘your_data.txt', delimiter=r"\s+”)
X = df.values
2. How I can calculate Rand index for my data?
After you ran the clustering, you can use the “adjusted_rand_score” function,
e.g., see
http://scikit-learn.org/stable/modules/clustering.html#adjusted-rand-score
3. How to use make_blobs command for my data?
The make_blobs command is just a utility function to create toydatasets, you
wouldn’t need it in your case since you already have “real” data.
Best,
Sebastian
On Mar 30, 2017, at 4:51 AM, Shuchi Mala <shuchi...@gmail.com> wrote:
Hi everyone,
I have the data with following attributes: (Latitude, Longitude). Now I am
performing clustering using DBSCAN for my data. I have following doubts:
1. How can I add data to the data set of the package?
2. How I can calculate Rand index for my data?
3. How to use make_blobs command for my data?
Sample of my data is :
Latitude Longitude
37.76901 -122.429299
37.76904 -122.42913
37.76878 -122.429092
37.7763 -122.424249
37.77627 -122.424657
With Best Regards,
Shuchi Mala
Research Scholar
Department of Civil Engineering
MNIT Jaipur
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*PhD candidate & Research Assistant*
*Cooperative Institute for Research in Environmental Sciences (CIRES)*
*University of Colorado at Boulder*
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