Dbscan random state Using the same seed value, the same random dataset is generated on each run. 0), where the silhouette score alongside some other metrics is computed for DBSCAN cluster assignments. datasets import make_blobs from sklearn. 5, min_samples=5, metric='euclidean', verbose=False, random_state=None)¶ Perform DBSCAN clustering from vector array or distance matrix. Figure 1 gives an overview. from sklearn. random_state int, RandomState instance, default=None. Choosing these parameters can be challenging, especially when dealing with high-dimensional datasets. 98825191], [-0. 6. Finally these cluster labels are used to train a Random Forest classifier via supervised learning. This implementation bulk-computes all neighborhood queries, which increases the memory complexity to O(n. Classification# Random Forest Classification and Accuracy metrics#. Use the provided random state, only affecting other users of that same random state instance. 2. Dec 9, 2019 · DBSCAN has the inherent ability to detect outliers. 0. e. 917 Adjusted Rand Index: 0. shape[2])) # определение алгоритма DBSCAN с параметрами dbscan May 10, 2020 · Let’s plot our synthetic data (using our two continuous features as the x and y axes). cluster import DBSCAN dbscan = DBSCAN(random_state=0) dbscan. DBSCAN (eps=0. : Size of the two-level cell dictionary. normal(-30, 30),math. Dec 26, 2023 · Environmental Studies: DBSCAN can be used in environmental monitoring, for example, to cluster areas based on pollution levels or to identify regions with similar environmental characteristics. Dec 6, 2014 · from sklearn. Notes. pi/n*x)*r+np. cluster import DBSCAN dbscan = DBSCAN(random_state=111) dbscan. If sample_size is None, no sampling is used. The algorithm is composed of three main sub-algorithms: t-SNE, DBSCAN, and Random Forest classifier. 03, random_state=4) # Apply DBSCAN dbscan = DBSCAN(eps=0. In summary, the choice between DBSCAN and K-Means depends on the specific characteristics of the dataset. The basic idea behind DBSCAN is derived from a human intuitive clustering method. May 11, 2020 · KMeans(n_clusters=8, init='k-means++', n_init=10, max_iter=300, tol=0. The Oct 3, 2023 · These problems are solved by DBSCAN, but what does it mean and how it works? DBSCAN is a density-based clustering algorithm, which means it identifies clusters based on the density of data points. Oct 10, 2023 · The experimental results show that STRP-DBSCAN effectively reduces the clustering time of spatial-temporal trajectory data by up to 96. datasets import make_moons X, y = make_moons(n_samples=200, noise=0. Jun 9, 2022 · 3D点群に対してk-means, DBSCAN, HDBSCANでクラスタリングをしました。 k-meansはクラスタ数、DBSCANはepsとminPtsをあらかじめ設定しなければいけないのに対して、HDBSCANはminPts一つだけなので容易にクラスタリングできました。 random_state int, RandomState instance or None, default=None. Feb 11, 2020 · If General Motors’ stock falls, the investor profits and if Ford’s stock rises, the investor also profits. preprocessing import StandardScaler Générer des exemples de données Jul 25, 2023 · d) random_state: Seed value used to check random numbers. Pass an int for reproducible output across multiple function calls. samples_generator import make_blobs from sklearn. The first one is the data points that I am assigning to the variable X and the second thing is it will return an array of labels. random_state int, RandomState instance or None, default=None. However, there is a bit of mixture evident in the blue and red blobs and it will be interesting to explore how our different clustering approaches can capture this. This function will return two things. The Random Forest algorithm classification model builds several decision trees, and aggregates each of their outputs to make a prediction. Step 1: Import Necessary Libraries import numpy as np import matplotlib. It divided the nodes to “core point”; “border point”, and “outlier point” By given the pre-assigned diameters (of the sphere) and number of the adjacent nodes, it scan the nodes randomly. The size of the sample to use when computing the Silhouette Coefficient on a random subset of the data. 05, random_state=0) # Perform DBSCAN clustering dbscan = DBSCAN(eps=0. 626 Jan 24, 2021 · I have clustered Iris data set with DBSCAN. edu, madian. Determines random number generation for dataset shuffling and noise. I made the following cluster through dbscan skelearn My data is a numpy array: array([[-0. This algorithm is very sensitive to the parameters ε and minPts. 5, min_samples=5, metric='euclidean', algorithm='auto', leaf_size=30, p=None, random_state=None) metric: string, or callable. , the ground truth at the beginning. While DBSCAN doesn’t require setting the number of clusters explicitly, setting eps implicitly controls how many clusters will be found. Using a Random Forest and DBSCAN Kunho Kim‡, Madian Khabsa⇤, C. 8. # DBSCAN snippet from the question from sklearn. Returns: X ndarray of shape (n_samples, 2) The generated samples. 8. 5 from the condensed cluster tree, but leave HDBSCAN* clusters that emerged at distances greater than 0. : For an example, see Demo of DBSCAN clustering algorithm. A pseudo random number generator used for the initialization of the lobpcg eigenvectors decomposition when eigen_solver == 'amg', and for the K-Means initialization. Nov 6, 2021 · DBscan is cluster a group of nodes by the spatial distribution density. In our case: We create 200 samples (n Jan 7, 2015 · I am using DBSCAN to cluster some data using Scikit-Learn (Python 2. Sep 26, 2021 · DBSCAN Advantages. fit_transform(val) db = DBSCAN(eps=3, min_samples=4). 01525 Skip to main content Stack Overflow DBSCAN (Density-Based Spatial Clustering of Applications with Noise) DBSCAN is a popular clustering algorithm used in data mining and machine learning. Unlike K-Means, there is no predict method. 626 May 8, 2020 · DBSCAN (Density-based Spatial Clustering of Applications with Noise) は非常に強力なクラスタリングアルゴリズムです。 この記事では、DBSCANをPythonで行う方法をプログラムコード付きで紹介し、DBSCANの長所と短所をデータサイエンスを勉強中の方に向けて解説します。 Return clustering given by DBSCAN without border points. fit(X) However, I found that there was no built-in function (aside from "fit_predict") that could assign the new data points, Y, to the clusters identified in the original data, X. Oct 17, 2024 · random_state is used to produce the same results. reshape(img,(img. All you need to do it re-assign val and y_pred to ignore the noise labels. Aug 31, 2013 · DBSCAN indeed does not impose a total size constraint on the cluster. 16, will be removed in version 0. 5, noise=0. if you have a dataset like [1,2,3,4,5], arrangement of its elements can be randomized up to 5! orders (factorial of the length) which in this example is 120. Here's a brief description of the dataset: The make_moons function generates a binary classification dataset that resembles two interleaving half moons. 883 V-measure: 0. Self cluster forming; Unlike its much more famous counterpart, k means, DBSCAN does not require a number of clusters to be defined beforehand. datasets import make_moons from sklearn. factor=0. Wiki states:. normal(-30, 30)) for x in range (1,n+ 1)] # Step 2-# One circle won’t be sufficient to see the clustering ability of DBSCAN. randn(1000, 2) severity = np. RandomState instance. fit May 6, 2021 · Now I want to apply the transformed data to DBSCAN like >>> dbscan = DBSCAN(eps=0. The metric to use when calculating distance between instances in a feature array. The epsilon value is best interpreted as the size of the gap separating two clusters (that may at most contain minpts-1 objects). This repository hosts fast parallel DBSCAN clustering code for low dimensional Euclidean space. cluster import DBSCAN from sklearn import metrics from sklearn. Lee Giles†‡ ‡Computer Science and Engineering ⇤Microsoft Research †Information Sciences and Technology One Microsoft Way The Pennsylvania State University Redmond, WA 98005, USA University Park, PA 16802, USA kunho@cse. Finally these cluster labels are used to train a Random Forest classifier via supervised learning. print(__doc__) Apr 10, 2023 · # Generate random data with two moon-shaped clusters X, y = make_moons(n_samples=1000, noise=0. khabsa@microsoft. • DBSCAN is computationally expensive and can be slow on large datasets. Demo of DBSCAN clustering algorithm. They generate a set of data points: from sklearn. so for example random_state = 0 is something like [2,3,5,4,1 The output of DBSCAN's fit_predict is evaluated using the Adjusted Random Index function. The algorithm iteratively assigns data points to the nearest cluster centroid and updates the centroids based on the mean of the assigned points. Scale factor between inner and outer circle in the range [0, 1). fit(data3) data3 is a pandas dataframe: FAC1_2 FAC2_2 0 -0. sin(2 *math. The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors. shape[1], img. However, instead of generating random sample data, I want to import my own . We will set the minPts parameter to 5 and the "eps" parameter to 0. There are 3 quite distinct blobs shown in blue, red, and yellow. Nov 24, 2024 · DBSCAN stands for D ensity-B ased S patial C lustering of A pplications with N oise. Sep 29, 2024 · X, _ = make_moons(n_samples=200, noise=0. fit_predict(X_trans_svd) but my kernel crashes. While the DBSCAN algorithm is very intuitive, it has its own quirks. DBSCAN - Density-Based Spatial Clustering of Applications with Noise. Jun 10, 2018 · Therefore, the performance results show that RP-DBSCAN significantly outperforms the state-of-the-art algorithms by up to 180 times. May 27, 2018 · In addition, we build and broadcast a highly compact summary of the entire data set, which we call a two-level cell dictionary, to supplement random partitions. dbscan¶ sklearn. RandomState, optional: Deprecated and ignored as of version 0. com, giles@ist Demo of DBSCAN clustering algorithm# DBSCAN (Density-Based Spatial Clustering of Applications with Noise) finds core samples in regions of high density and expands clusters from them. cluster import DBSCAN from sklearn import metrics from sklearn. datasets import make_blobs from from sklearn. For datasets with noise and arbitrary shapes, DBSCAN is often the better choice. datasets. It is used to partition data into K distinct clusters. A random forest classifier is trained to classify whether each pair of inventor records is the same person. Selecting alpha ¶ A numpy. it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away) Jul 6, 2024 · I think I have understood the DBScan algorithm for 2D data points. The primary parameters to consider are eps (the maximum distance between two samples for them to be considered as in the same neighborhood) and min_samples (the number of samples in a neighborhood for a point to be considered as a core point). eps: determines what it means for points to be “close” Démo de l'algorithme de clustering DBSCAN. 22725194, -0. The goal is to help learners understand and practice how to effectively tune these parameters to refine 19 hours ago · その中で「DBSCAN（Density-Based Spatial Clustering of Applications with Noise）」というアルゴリズムは、特に密度に基づいたクラスタリングを得意とする手法です。 1. preprocessing import StandardScaler from pylab import * # Generate sample data centers = [[1, 1 The lesson provides an in-depth examination of the DBSCAN algorithm’s parameters. preprocessing import StandardScaler val = StandardScaler(). Oct 3, 2023 · Introduction: Anomaly detection is a critical component of data analysis across various domains such as finance, cybersecurity, healthcare, and more. Existing algorithms do not make good of the data. Conclusion. in 1996. Nov 21, 2024 · Applying DBSCAN in Python. Since points that are outliers will fail to belong to any cluster. We assume that the data is in the usual format, with rows representing individual data points and columns representing the features. See Glossary. This example shows characteristics of different clustering algorithms on datasets that are “interesting” but still in 2D. Inner Workings of DBSCAN. The DBSCAN algorithm is use for inventor record clustering, and its distance function is derived using the random forest classifier. Used when sample_size is not None. 5, min_samples=5) >>> clusters = dbscan. There's an old discussion from 2012 on the scikit-learn repository about this. For an example, see Demo of DBSCAN clustering algorithm. Therefore I am different modifications of the following code, which is putting out the last plot from the second for loop, formatted to the lower right corner. 953 Completeness: 0. DBSCAN(eps=0. dbscan(X, eps=0. The label is -1 if a point is unassigned. 2, min_samples=5) clusters = dbscan. Finding Best hyperparameters for DBSCAN using Silhouette Coefficient The Silhouette Coefficient is calculated using the mean intra-cluster distance (a) and the mean nearest-cluster distance (b) for each sample. Jan 7, 2025 · Performance on Noise: DBSCAN effectively identifies noise, while K-Means may incorporate outliers into clusters, skewing results. 18. 2% compared to parallel DBSCAN and the state-of-the Dec 27, 2014 · ok merci, en faite je veux comprendre quelques méthodes utilisées dans une implémentation de DBSCAN sous python !!! le code complet : print(__doc__) import numpy See the documentation of DBSCAN (emphasis added): class sklearn. Comparing different clustering algorithms on toy datasets#. 227252 -0. check_random_state is used internally to validate the input random_state and return a RandomState instance. Using earlier work on author name disambiguation, we apply it to inventor name disambiguation. Jul 19, 2023 · Here is the generated dataset. import numpy as np from sklearn. Downside is that it assumes this spherical structure and you need to input the number of centroids. Jun 3, 2024 · CountVectorizer(stop_words='english'): Converts the collection of text documents to a matrix of token counts, excluding common English stop words. 5, min_samples=5, metric='minkowski', algorithm='auto', leaf_size=30, p=2, random_state=None)¶ Perform DBSCAN clustering from vector array or distance matrix. Sep 29, 2014 · random state has a meaning beyond its application in sklearn (for example it is also used in Random Forest method). 05, random_state=0) Perform DBSCAN clustering. 1 The Algorithm. Oct 10, 2023 · Trajectory clustering algorithms analyze the movement trajectory of the target objects to mine the potential movement trend, regularity, and behavioral patterns of the object. 05, random_state=0) # Apply DBSCAN dbscan = DBSCAN(eps=0. It forms clusters using the rules we defined above Aug 29, 2023 · The primary idea behind DBSCAN is to define clusters as dense regions of data points separated by sparser regions. Return clustering that would be equivalent to running DBSCAN* for a particular cut_distance (or epsilon) DBSCAN* can be thought of as DBSCAN without the border points. scatter(X Apr 9, 2019 · I want to do some data exploration using this DBSCAN clustering alogrithm example by Scikit Learn. 4, random_state=0 ) X = StandardScaler(). When max_features < n_features, the algorithm will select max_features at random at each split 8. Unsupervised learning; The DBSCAN algorithm requires no labels to create clusters hence it can be applied to all sorts of data. 0001, precompute_distances='auto', verbose=0, random_state=None, copy_x=True, n_jobs=None, algorithm='auto') random_state. org Sep 23, 2024 · DBSCAN smartly leaves these out, avoiding the pitfalls KMeans falls into with outliers. 1. We can set it to any number we want. DBSCAN. tar. If seed is an int, return a new RandomState instance seeded with seed. The next step is to perform DBSCAN clustering on the dataset. preprocessing import StandardScaler centers = [[1, 1], [-1, -1], [1, -1]] X, labels_true = make_blobs( n_samples=750, centers=centers, cluster_std=0. We can consider the example in scikit-learn. Out: Estimated number of clusters: 3 Homogeneity: 0. It is particularly effective at finding clusters of arbitrary shapes and sizes in spatial data. ↳ 1 cell hidden Jul 7, 2021 · 2. From an implementors point of view, DBSCAN is not deterministic: Different implementations can both be correct, yet yield slightly different results. Suffice to say, when you're using a clustering algorithm, the concept of train/test splits is less defined. labels_ # re-assign y_pred and core (as val) y_pred Jul 27, 2022 · Hashes for pyspark-dbscan-1. from enhanced_adaptive_dbscan import EnhancedAdaptiveDBSCAN import numpy as np # Generate synthetic data X = np. Oct 8, 2017 · In contrast to k-means, where I must consider different random seeds, I do not need to do shuffling for DBSCAN. Here is the code I have used import numpy as np from sklearn. So if I compare my results to the results of someone else, I cannot blindly require May 3, 2023 · $\begingroup$ Hi @gianMa thanks for your question. Will the centroids change or not due to the fixed random_state?? – Apr 12, 2020 · There are three types of clustering algorithms: * Prototype based clustering: k-means which clusters into spherical shapes based on a specified number of cluster centroids. 5, min_samples=5, metric='euclidean', algorithm='auto', leaf_size=30, p=None, random_state=None) [源代码] ¶ Perform DBSCAN clustering from vector array or distance matrix. In DBSCAN, you do not have to specify the number of clusters! Instead, you have to tune eps and min_samples. cluster import DBSCAN import numpy as np # загрузка изображения img = plt. fit(X) print_cluster_stats(dbscan) Number of clusters: 3 Number of noise points: 14 This time the algorithm identifies three clusters and also detects 14 outliers. If seed is None, return the RandomState singleton used by np. algorithm {‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, default=’auto’. Sep 19, 2024 · Plotting Cluster Boundaries with DBSCAN. This contrasts traditional clustering algorithms like k-means, which assume clusters as spherical or convex shapes and can struggle with non-linear and irregular cluster structures. At times I get different number of clusters using the same parameter specifications (so instead of getting 5 clusters as in the original solution, I get 4, or 6). preprocessing import StandardScaler Feb 23, 2024 · K-means Clustering. This algorithm is good for data which contains clusters of similar density. So, we don’t provide the labels i. Data Set ε 1/8 · ε May 2, 2023 · • DBSCAN requires tuning of two parameters: the minimum number of points required to form a dense region (minPts) and the radius of each dense region (epsilon). Use an int to make the results deterministic across calls (See Glossary). Returns: X ndarray of shape (n_samples, 2) @AndreasMueller if I use 10 n_init and specify the random_state, as n_init=10, random_state=3425, does this make sense? n_init is the number of time the k-means algorithm will be run with different centroid seeds. Feb 27, 2024 · The effect of the parameters ε and minPts on the algorithm. It covers the theoretical aspects of the parameters, their role in determining the characteristics of clusters, and practical examples showing the outcome of different parameter values on clustering results. 1. sklearn. 5 untouched. DBSCAN stands for Density-Based Spatial Clustering of Applications with Noise and it is hands down the most well-known density-based clustering algorithm. See full list on geeksforgeeks. 7): from sklearn. 3, min_samples=5) random_state int, RandomState instance or None, default=None. As you can see, it’s composed of five different structures: two circles, two moons, and a blob, each with a different density. DBSCAN¶ class sklearn. d) where d is the average number of neighbors, while original DBSCAN had memory complexity O(n). I want to iterate over different parameter values for eps. Therefore, the trajectory clustering algorithm has a wide range of applications in the fields of traffic flow analysis, logistics and transportation management, and crime analysis. We will use the DBSCAN class from the scikit-learn library. random. Let’s apply DBSCAN on a synthetic dataset using Python’s scikit-learn library. The features are always randomly permuted at each split, even if splitter is set to "best". The dataset is visualized by following the following line of Oct 17, 2023 · dbscan = DBSCAN(eps=0. It’s particularly useful for datasets with varying density. fit_transform(X) DBSCAN can be used for any standard application of clustering, but it is worth understanding Jan 7, 2025 · To effectively optimize DBSCAN clustering, it is crucial to focus on hyperparameter tuning strategies that enhance performance. 883 Silhouette Coefficient: 0. . Here’s how you can visualize the cluster boundaries using DBSCAN: Step 1: Import Required Libraries Python Jul 12, 2023 · I have a dataset with 4 features,with features (1,4) and (2,4) clearly separable. Dec 17, 2024 · Let's apply DBSCAN on a sample dataset to see how we can discover clusters: from sklearn. seed(42) # Function for creating datapoints in the form of a circle def PointsInCircum (r, n = 100): return [(math. Controls the randomness of the estimator. datasets import make_blobs from sklearn. K-means clustering is one of the first and most popular unsupervised Machine Learning algorithms. Recherche des échantillons de base de haute densité et étend les clusters à partir d'eux. * Hierarchical clustering: Agglomerative clustering does not require specifying the number of clusters up front and the shuffle=True, random_state=1) X = StandardScaler(). 2% and 31. For the next step, the DBSCAN algorithm is applied to the low-dimensional embedding to produce cluster labels for each data point. Calling the function multiple times will reuse the same instance, and will produce different results. The DBSCAN algorithm is more flexible than KMeans as it doesn’t require specifying the number of clusters in advance. psu. pyplot as plt from sklearn. cluster. cluster import DBSCAN from sklearn. Determines random number generation for selecting a subset of samples. 68548221], [ 0. 3) dbscan. Aug 14, 2013 · following the example Demo of DBSCAN clustering algorithm of Scikit Learning i am trying to store in an array the x, y of each clustering class . DBSCAN Algorithm is a density-based clustering algorithm that works on the assumption that clusters are dense regions in space separated by regions of lower density. 05, random_state=42) This code creates a synthetic dataset using the make_moons function from scikit-learn. I am trying to use DBSCAN to come up with the Clusters, however I am unable to create satisfatocty clusters. Apr 30, 2020 · You can reuse the same code from your KMeans model. randint(1 random_state: numpy. X_counts: The resulting sparse matrix where each K-Means vs. imread('image. Mar 18, 2024 · For the next step, the DBSCAN algorithm is applied to the low-dimensional embedding to produce cluster labels for each data point. 685482 1 0. To help us find historically correlated securities, we can use DBSCAN clustering. y ndarray of shape (n_samples,) Mar 13, 2016 · Code on python, cluster image. The code automatically uses the available threads on a parallel shared-memory machine to speedup DBSCAN clustering. 1, min_samples=5) clusters random_state: numpy. These assignments include some Noise check_random_state# sklearn. pyplot as plt # Generate sample data X, _ = make_moons(n_samples=300, noise=0. The Random Forest model can thus infer cluster labels directly from the raw input data. 29117618, -0. DBSCAN due to the difference in implementation over the non-core This will basically extract DBSCAN* clusters for epsilon = 0. Then, we develop a novel parallel DBSCAN algorithm, Random Partitioning-DBSCAN (shortly, RP-DBSCAN), that uses pseudo random partitioning together with a two-level cell dictionary. jpg') # преобразование изображения в массив точек points = np. As such these results may differ slightly from cluster. See Combining HDBSCAN* with DBSCAN for a more detailed demonstration of the effect this parameter has on the resulting clustering. Finds core samples of high density and expands clusters from them. fit(val) labels = db. It was proposed by Martin Ester et al. It stems from a paper presented in SIGMOD'20: Theoretically Efficient and Practical Nov 7, 2019 · I checked the DBSCAN scikit questions (which are very old) already but my code is giving an error: DBSCAN() got an unexpected argument eps The input is not my actual input just test values but I DBSCAN Analogy. I also tried converting it back to a df and apply it to DBSCAN Estimated number of clusters: 3 Homogeneity: 0. It is useful if we want to reproduce exact clusters over and over again. check_random_state (seed) [source] # Turn seed into a np. Now, remember that DBSCAN is unsupervised learning. The values obtained by sklearn and cuml's adjusted random metric are compared below. I am not so good at python so all my attempts failed. 952 Adjusted Mutual Information: 0. Nov 4, 2023 · はい、dbscanは密度ベースのクラスタリング手法の一つで、密度ベースとはデータポイントが密集している領域をクラスタとみなす手法のことを言います。 dbscan以外にもいくつかの密度ベースのクラスタリング手法が存在します。以下にその例を挙げます： Jun 15, 2020 · 3 Introducing DBSCAN. Consider DBSCAN in a social context: Social butterflies (🦋): Core points; Friends of social butterflies who are not social butterflies: Border points; Lone wolves (🐺): Noise points; Two main hyperparameters. May 19, 2021 · I am using this code for DBSCAN algorithm. csv file. utils. DBSCANとは？ DBSCANは「密度」に基づいてデータをクラスタリングするアルゴリズムです。 Mar 29, 2019 · DBSCAN, as implemented in scikit-learn, is a transductive algorithm, meaning you can't do predictions on new data. shape[0]*img. cluster import DBSCAN import matplotlib. Parameters: seed None, int or instance of RandomState. gz; Algorithm Hash digest; SHA256: db7ad092b66dea00974b51fea6580ba2be3952c350a1acf7b25322800e052041: Copy : MD5 Aug 3, 2018 · In the next section, you will get to know the DBSCAN algorithm where the ɛ-ball is a fundamental tool for defining clusters. If you have a look at the picture below you can easily identify 2 clusters along with several points of noise, because of the differences in the density of points. 01525107, -0. utils. Pass an int for reproducible Sep 30, 2018 · After twisting eps and min_samplesfor some time, I got some fairly consistent clusters, still including some noise points. This is setting a random seed. DBSCAN does not use random initialization. fit_predict(X) # Visualize the results plt. Unlike K-Means, DBSCAN doesn’t have to assign all points to clusters. np. factor float, default=. DBSCAN Clustering : A Brief Explanation Dec 13, 2022 · I stumbled across this example on scikit-learn (1. cos(2 *math. However, past prices aren’t always indicative of future trends, so this strategy still carries risk. zstf lgfmp mphnnbj zvt kjl abjdzr bbel exrwpp zgwptx ogckl