Hyper parameter tuning in dbscan clustering

Hi everybody. I read the article in "https://www.kaggle.com/aeyjpn/women-love-shopping-clustering-with-dbscan" that about customer segmentation by dbscan clustering. In this article there is no comment about dbscan hyper parameter tuning and the writer considered the eps = 0.5 & min-samples = 4.  I did this example by my own way. My code is below: STEP 1: read a csv file into a data frame (df_data). STEP 2: Standardize data in df_data by StandardScaler. STEP 3: Hyper parameter tuning: from sklearn.cluster import DBSCAN from sklearn.metrics import silhouette_score range_min = [x for x in range(2, 51, 1)] range_eps = [x / 100.0 for x in range(1, 51, 1)] + \
                     [y / 10.0 for y in range(1, 51, 1)] + \
                     [round(z, 2) for z in np.arange(1.10, 1.31, 0.01)] dic = {} for m in range_min:

    for e in range_eps:

        model_1 = DBSCAN(eps = e, min_samples = m).fit(df_data)

        core_samples_mask = np.zeros_like(model_1.labels_, dtype = bool)

        core_samples_mask[model_1.core_sample_indices_] = True

        labels = model_1.labels_

        if len( set(labels) ) > 1:

            silhouette_Avg = silhouette_score(df_data, labels)

            if silhouette_Avg > 0:

                dic[str(m) + " - " + str(e)] = silhouette_Avg

                print("min-sample value is: " + str(m) + " eps value is: " + str(e) , "The avearge silhouette_score is :",                               silhouette_Avg)   max_key = max(dic, key = dic.get) print("parameter values are: ", max_key) print("maximum silhouette score value is: ", dic[max_key]) For eps = 0.5 & min-samples = 4 the silhouette_score is : 0.15198104684634364

BUT

For eps = 1.22 & min-samples = 13 the silhouette score value is: 0.2912153228904898

As we can see 0.29 is greater than 0.15 so I consider eps = 1.22 & min-samples = 13 then run dbscan clustering with these parameter values. STEP 4:  from sklearn import metrics model = DBSCAN(eps = 1.22, min_samples = 13).fit(df_data) core_samples_mask = np.zeros_like(model.labels_, dtype = bool)

core_samples_mask[model.core_sample_indices_] = True

labels = model.labels_ # Get number of clusters in labels, ignoring noise if present. n_clusters = len(set(labels)) - (1 if -1 in labels else 0) # Get number of outliers n_outlier = list(labels).count(-1)

print(set(labels))

print("\n") print("Estimated number of clusters: %d" % n_clusters) print("\n") print("Estimated number of outlier points: %d" % n_outlier) print("\n")

OUTPUT:

{0, -1}

Estimated number of clusters: 1
Estimated number of outlier points: 3
Silhouette Coefficient: 0.291

In that article number of clusters are 4 and number of outliers are: 54 

that are different with my result.

Could you please check my code & show my mistake?

If you know a better way for hyper parameter tuning in dbscan please let me know.

Thank you.