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It is difficult for people to obtain the required information directly from the original data itself. Machine learning is to convert disordered data into useful information. Clustering is an unsupervised machine learning technique that groups similar objects into the same cluster. Cluster2A combines the two most popular clustering algorithms, K-means and DBSCAN, to help you discover interesting patterns in the data.

For example, Cluster2A can perform cluster analysis based on customer consumption behavior and provide results for customer segmentation. Customer segmentation is the use of specific characteristics to identify and organize customers. These characteristics can be demographic, behavioral/psychological characteristics and geographic location. Customer segmentation can identify customers and provide products and services tailored to their needs. This personalization will provide you with a competitive advantage, increase customer conversion rates and brand loyalty.

Cluster2A contains four tools:

Growth Rate Data:
Add the growth rate data you want to analyze.
You can select any data for 12 consecutive periods for analysis. The most commonly used data include monthly material purchase prices, monthly product sales, monthly customer purchases and company annual operating income.
For example, you can perform cluster analysis based on the monthly purchase data of VIP customers. Cluster2A will automatically calculate each customer's purchase growth rate, purchase volatility and the growth rate per unit of volatility, and make clustering recommendations.

Feature Value Data:
Add the feature value data you want to analyze.
You can select any three feature values for analysis. The most commonly used feature values are as follows: Demographics: For example, age, gender, income, education, nationality and family size. Behavior/Psychology: For example, consumption style (RFM model) and personality type (DISC model). Geography: For example, country, region and city. Statistics/Finance: For example, mean, standard deviation, Sharpe ratio, β, α and R-squared.
For example, you can perform cluster analysis based on the three main buying characteristics of customers, RFM (Recency, Frequency, Monetary).

K-means Model:
Find the cluster centroid and assign each sample to the closest centroid.
The K-means algorithm requires the number of clusters to be specified. The main goal is to find a representative data point (called centroid) in a large amount of high-dimensional data, and then assign each data point to the nearest centroid. Cluster2A uses K-means++ to select the initial cluster centers to improve the convergence speed.

Divide the dense sample area into the same cluster and identify outliers.
Unlike K-means, DBSCAN does not need to specify the number of clusters to be generated. The DBSCAN algorithm processes data points based on density, mainly dividing sufficiently dense points in the feature space into the same cluster, and can identify outliers that do not belong to any cluster, which is very suitable for detecting outliers.

What’s New

Version 1.0.1

1. Bug fixes and improvements.
2. Added support languages: Japanese, Korean, German and French.

App Privacy

The developer, Chu-Yi Chang, indicated that the app’s privacy practices may include handling of data as described below. For more information, see the developer’s privacy policy.

Data Not Collected

The developer does not collect any data from this app.

Privacy practices may vary, for example, based on the features you use or your age. Learn More


  • Family Sharing

    With Family Sharing set up, up to six family members can use this app.

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