Falanqaynta Cluster-ka ee Python

Falanqaynta cluster-ka waa farsamo loo adeegsado in lagu kala saaro xogta kooxo (clusters) iyadoo lagu salaynayo isku ekaanshaha. Python, waxaad u isticmaali kartaa maktabado sida scikit-learn si aad u samayso falanqaynta cluster-ka.

Faa'iidooyinka:

• Waxay kaa caawinaysaa inaad ogaato qaabab qarsoon ee xogta.
• Waxay faa'iido u leedahay suuq-geynta, aqoonsiga macaamiisha, iyo in ka badan.

Tusaale: K-Means Clustering

K-Means waa algorithm caan ah oo loo isticmaalo falanqaynta cluster-ka. Wuxuu u baahan yahay inaad cayimto tirada clusters (K) ee aad rabto.

from sklearn.cluster import KMeans
import numpy as np

# Xogta tusaale
X = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])

# Samaynta K-Means model
kmeans = KMeans(n_clusters=2, random_state=0, n_init='auto')

# Tababarida modelka
kmeans.fit(X)

# Ogaanshaha labels-ka cluster-ka ee xog kasta
labels = kmeans.labels_

# Helitaanka bartamaha cluster-ka
centroids = kmeans.cluster_centers_

print("Labels:", labels)
print("Centroids:", centroids)

Sharaxaada koodhka:

• Waxaan soo dejinaynaa KMeans algorithm.
• Waxaan abuureynaa xog tusaale ah oo loo yaqaan X.
• Waxaan samaynaynaa KMeans model oo leh 2 clusters.
• Waxaan tababaraynaa modelka annagoo isticmaalayna xogta X.
• Waxaan helaynaa labels-ka cluster-ka iyo bartamaha cluster-ka.

Hababka kale ee Clustering

Waxaa jira habab kale oo badan oo loo isticmaali karo clustering, sida:

• Hierarchical Clustering: Wuxuu abuuraa hierarchy clusters ah.
• DBSCAN: Wuxuu aqoonsadaa clusters iyadoo lagu salaynayo cufnaanta xogta.

Doorashada algorithm-ka saxda ah waxay ku xiran tahay nooca xogtaada iyo hadafkaaga.