DSBCAN, short for Density-Based Spatial Clustering of Applications with Noise, is the most popular density-based clustering method. Density-based clustering algorithms attempt to capture our intuition that a cluster — a difficult term to define precisely — is a region of the data space where there are lots of points, surrounded by a region where there are few points. DBSCAN does this by partitioning the input data points into three types:

  • Core points have a large number of other points within a given neighborhood. The parameter min_core_neighbors defines how many points counts as a "large number", while the radius parameter defines how large the neighborhoods are around each point. Specifically, a point is in the neighborhood of point

    if radius, where is a user-specified distance function.

  • Boundary points are within distance radius of a core point, but don't have sufficient neighbors of their own to be considered core.

  • Noise points comprise the remainder of the data. These points have too few neighbors to be considered core points, and are further than distance radius from all core points.

Clusters are formed by connecting core points that are neighbors of each other, then assigning boundary points to their nearest core neighbor's cluster. Noise points are left unassigned.

DBSCAN tends to be slower than K-means because it requires computation of the similarity graph on the input dataset, but it has several major conceptual advantages:

  • The number of clusters does not need to be known a priori; DBSCAN detects the optimum number of clusters automatically for the given values of min_core_neighbors and radius.

  • DBSCAN recovers much more flexible cluster shapes than K-means, which can only find spherical clusters.

  • DBSCAN intrinsically finds and labels outliers as such, making it a great tool for outlier and anomaly detection.

  • DBSCAN works with any distance function. Note that results may be poor for distances that do not obey standard properties of distances, i.e. symmetry, non-negativity, triangle inequality, and identity of indiscernibles. The distances "euclidean", "manhattan", "jaccard", and "levenshtein" will likely yield the best results.

Basic usage

To illustrate the basic usage of DBSCAN and how the results can differ from K-means, we simulate non-spherical, low-dimensional data using the scikit-learn datasets module.1

import turicreate as tc
from sklearn.datasets import make_moons

data = make_moons(n_samples=200, shuffle=True, noise=0.1, random_state=19)
sf = tc.SFrame(data[0]).unpack('X1')

dbscan_model = tc.dbscan.create(sf, radius=0.25)
Class                                   : DBSCANModel

Number of examples                      : 200
Number of feature columns               : 2
Max distance to a neighbor (radius)     : 0.25
Min number of neighbors for core points : 10
Number of distance components           : 1

Training summary
Total training time (seconds)           : 0.1954
Number of clusters                      : 2

Accessible fields
   cluster_id                           : Cluster label for each row in the input dataset.

Like the K-means model, the assignments of points to clusters is in the models' cluster-id field. The second column shows the cluster assignment for the row index of the input data indicated by the first column. The third column shows whether DBSCAN considers the point core, boundary, or noise. Noise points are not assigned to a cluster, which is encoded as a "missing" value in the 'cluster_id' column.

| row_id | cluster_id | type |
|  175   |     1      | core |
|  136   |     0      | core |
|   33   |     1      | core |
|  113   |     0      | core |
|  110   |     1      | core |
[5 rows x 3 columns]
| row_id | cluster_id |   type   |
|   53   |     0      | boundary |
|   4    |     1      | boundary |
|   43   |     0      | boundary |
|  116   |     0      | boundary |
|   74   |    None    |  noise   |
[5 rows x 3 columns]

Because we generated 2D data, we can plot it and color the points according to the cluster assignments generated by our DBSCAN model. The first step is to join the cluster results back to the original data. Please note: DBSCAN scrambles the row order - be careful!

Next we define boolean masks so we can plot the core, boundary, and noise points separately. Boundary points are drawn smaller than core points, and (unclustered) noise points are left black. Some plotting code is omitted for brevity.

import matplotlib.pyplot as plt

sf = sf.add_row_number('row_id')
sf = sf.join(dbscan_model['cluster_id'], on='row_id', how='left')
sf = sf.rename({'cluster_id': 'dbscan_id'})

core_mask = sf['type'] == 'core'
boundary_mask = sf['type'] == 'boundary'
noise_mask = sf['type'] == 'noise'

fig, ax = plt.subplots()
ax.scatter(sf['X1.0'][core_mask], sf['X1.1'][core_mask], s=80, alpha=0.9,
           c=sf['dbscan_id'][core_mask], cmap=plt.cm.Set1)

ax.scatter(sf['X1.0'][boundary_mask], sf['X1.1'][boundary_mask], s=40,
           alpha=0.7, c=sf['dbscan_id'][boundary_mask], cmap=plt.cm.Set1)

ax.scatter(sf['X1.0'][noise_mask], sf['X1.1'][noise_mask], s=40, alpha=0.7,


For comparison, K-means cannot identify the true clusters in this case, even when we tell the model the correct number of clusters.

kmeans_model = tc.kmeans.create(sf, features=['X1.0', 'X1.1'], num_clusters=2)
sf['kmeans_id'] = kmeans_model['cluster_id']['cluster_id']

fig, ax = plt.subplots()
ax.scatter(sf['X1.0'], sf['X1.1'], s=80, alpha=0.9, c=sf['kmeans_id'],


Setting key parameters

DBSCAN is particularly useful when the number of clusters is not known a priori, but it can be tricky to set the radius and min_core_neighbors parameters. To improve the quality of DBSCAN results, try the following:

  • When radius is too large or min_core_neighbors is too small, every point being labeled as a core point, often connected as a single cluster. If your created model has a single cluster with only core points (and you suspect this is incorrect), try decreasing the radius parameter or increasing the min_core_neighbors parameter.

  • Conversely, if DBSCAN labels all points as noise, with no clusters returned at all, try increasing the radius or decreasing the min_core_neighbors.

  • Use the Turi Create nearest neighbors toolkit to construct a similarity graph on the data and plot the distribution of distances with Canvas. This will give you a sense for reasonable values of the radius parameter, then the min_core_neighbors parameter can be tuned by itself for optimal results.

Choosing the distance function

DBSCAN is not restricted to Euclidean distances. The Turi Create implementation allows any distance function---including composite distances---to be used with DBSCAN. This allows for a tremendous amount of flexibility in terms of data types and tuning for optimal clustering quality. To demonstrate this flexibility we'll use DBSCAN with Jaccard distance to deduplicate wikipedia articles.

import turicreate as tc

sf = tc.SFrame.read_csv('wikipedia-data.csv', header=False)

This particular subset of wikipedia has over 72,000 documents; in the interest of speed for the demo we sample 20% of this. We also preprocess the data by constructing a bag-of-words representation for each article and trimming out stop words. See Turi Create's text analytics and SArray documentation for more details.

sf_sample = sf.sample(0.2)
sf_sample['word_bag'] = tc.text_analytics.count_words(sf_sample['X1'])
sf_sample['word_bag'] = sf_sample['word_bag'].dict_trim_by_keys(
                                    tc.text_analytics.stop_words(), exclude=True)

With our trimmed bag of words representation, Jaccard distance is a natural choice. For the purpose of deduplication we want to identify points as "core" if they have any near neighbors at all, so we set min_core_neighbors to 1. To define what we mean by "near" we set the radius parameter somewhat arbitrarily at 0.5; this means that two points are considered neighbors if they share 50% or more of the words present in either article.

wiki_cluster = tc.dbscan.create(sf_sample, features=['word_bag'],
                                distance='jaccard', radius=0.5,
Class                                   : DBSCANModel

Number of examples                      : 14265
Number of feature columns               : 1
Max distance to a neighbor (radius)     : 0.5
Min number of neighbors for core points : 1
Number of distance components           : 1

Training summary
Total training time (seconds)           : 35.1235
Number of clusters                      : 88

Accessible fields
   cluster_id                           : Cluster label for each row in the input dataset.

From the model summary we see there are 88 clusters in the set of 14,265 documents. For more detail on the distribution of cluster sizes, we can use the method.

|        item        |     value     | is exact |
|       Length       |     14265     |   Yes    |
|        Min         |      0.0      |   Yes    |
|        Max         |      87.0     |   Yes    |
|        Mean        | 32.3243243243 |   Yes    |
|        Sum         |    10764.0    |   Yes    |
|      Variance      | 699.186105024 |   Yes    |
| Standard Deviation | 26.4421274678 |   Yes    |
|  # Missing Values  |     13932     |   Yes    |
|  # unique values   |       88      |    No    |

Most frequent items:
| value | 1  | 10 | 5  | 19 | 9  | 36 | 64 | 62 | 11 | 23 |
| count | 33 | 29 | 18 | 15 | 14 | 13 | 9  | 7  | 7  | 5  |

|  0% |  1% |  5% | 25%  | 50%  | 75%  | 95%  | 99%  | 100% |
| 0.0 | 1.0 | 1.0 | 10.0 | 24.0 | 55.0 | 80.0 | 86.0 | 87.0 |

This indicates that of our 14,265 documents, 13,932 are considered noise (i.e. missing values), which in this context means they have no duplicates. The largest cluster has 33 duplicate documents! Let's see what they are. To do this we again need to join the cluster IDs back to our input dataset.

sf_sample = sf_sample.add_row_number('row_id')
sf_sample = sf_sample.join(wiki_cluster['cluster_id'], on='row_id', how='left')
In [98]: sf_sample[sf_sample['cluster_id'] == 1][['X1']].print_rows(10, max_row_width=80, max_column_width=80)
|                                        X1                                       |
| graceunitedmethodistchurchwilmingtondelaware it was built in 1868 and added ... |
| firstpresbyterianchurchdelhinewyork it was added to the national register of... |
| methodistepiscopalchurchofnorwich it was added to the national register of h... |
| odonelhouseandfarm it was listed on the national register of historic places... |
| saintpaulsepiscopalchurchwatertownnewyork it was listed on the national regi... |
| windsorhillshistoricdistrict it was added to the national register of histor... |
| pepperellcenterhistoricdistrict the district was added to the national regis... |
| eboardmanhouse it was built in 1820 and added to the national register of hi... |
| johnsoutherhouse the house was built in 1883 and added to the national regis... |
| ednastolikerthreedecker it was built in 1916 and added to the national regis... |
[33 rows x 1 columns]

It seems this cluster captures a set of article stubs that simply list when a physical structure was build and added to the National Register of Historic Places.

There are two important caveats regarding distance functions in DBSCAN:

  1. DBSCAN computes many pairwise distances. For dense data, Turi Create computes some distances much faster than others, namely "euclidean", "squared_euclidean", "cosine", and "transformed_dot_product". Other distances, as well as all distances with sparse data, may result in longer run times.

  2. DBSCAN does not explicitly require the standard distance properties (symmetry, non-negativity, triangle inequality, and identity of indiscernibles) to hold, but it is based on connecting high-density points which are close to each other into a single cluster. If the specified notion of closeness violates the usual distance properties, DBSCAN may yield counterintuitive results. We expect to see the most intuitive results with "euclidean", "manhattan", "jaccard", and "levenshtein" distances.

References and more information

results matching ""

    No results matching ""