clustering data based on multiple linear regression in Python

Question

I'd like to separate data and put them into 13 different set of variables like each red circle (see the image below). But I have no idea how to cluster the data based on multiple linear regression. Any idea how can I do this in Python?

Data set: https://www.dropbox.com/s/ar5rzry0joe9ffu/dataset_v1.xlsx?dl=

Code I am using now for clustering:

print(__doc__)

import openpyxl
import numpy as np
from sklearn.cluster import DBSCAN
import matplotlib.pyplot as plt


wb = openpyxl.load_workbook('dataset_v1.xlsx')
sheet = wb.worksheets[0]
ws = wb.active
row_count = sheet.max_row
data = np.zeros((row_count, 2))

index = 0
for r in ws.rows:
    data[index,0] = r[0].value
    data[index,1] = r[1].value
    index += 1


# Compute DBSCAN
db = DBSCAN(eps=5, min_samples=0.1).fit(data)
core_samples_mask = np.zeros_like(db.labels_, dtype=bool)
core_samples_mask[db.core_sample_indices_] = True
labels = db.labels_

# Number of clusters in labels, ignoring noise if present.
n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)
n_noise_ = list(labels).count(-1)

clusters = [data[labels == i] for i in range(n_clusters_)]
outliers = data[labels == -1]

# #############################################################################
# Plot result

# Black removed and is used for noise instead.
unique_labels = set(labels)
colors = [plt.cm.Spectral(each)
          for each in np.linspace(0, 1, len(unique_labels))]
for k, col in zip(unique_labels, colors):
    if k == -1:
        # Black used for noise.
        col = [0, 0, 0, 0.5]

    class_member_mask = (labels == k)

    xy = data[class_member_mask & core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=14)

    xy = data[class_member_mask & ~core_samples_mask]
    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),
             markeredgecolor='k', markersize=6)

plt.title('Estimated number of clusters: %d' % n_clusters_)
plt.show()

Answer 1

Define a threshold, say 50.

Begin a new "cluster" whenever y increases by more than 50. As long as values decrease, they are still in the previous "cluster".