Cluster2A vs Lean Fishbone Diagram Rpt Usage & Stats
For most people, it is difficult to obtain the information they need directly from raw data. Machine learning can transform 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.
K-means Model:
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.
DBSCAN Model:
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.
Growth data type:
You can select time series data with 12 periods, 24 periods, and 36 periods for analysis.
The most commonly used data are monthly material purchase prices, monthly product sales, monthly customer purchases, and the company's 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 data type:
You can select 2 to 10 characteristics for analysis.
The most commonly used characteristics 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 buying characteristics of customers, RFM (Recency, Frequency, Monetary).
- Apple App Store
- Free
- Productivity
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Fishbone Diagram is documenting by visually displaying potential causes and effects of a problem. This app provides fundamental information on how the Fishbone (or Cause and Effect) Diagram can be used to explore in increasing detail all the possible causes of a problem or issues in a standardized, customizable, and sharable format on the cloud. If done properly and completely, the cause(s) of the problem should be somewhere listed on the Diagram. This app is designed for anyone who wants to effectively solve problems via Total Employee Involvement. Engaging employees in the Fishbone analysis will help facilitate a greater number of potential solutions on how to improve a process or solve a problem.
The Dynamic Kanban Action Item Board allows Action Items to be visually managed using 4 “buckets” of To Do, In Progress, In Review, and Done allowing for Assignor to track progress by a simple drag and drop function to each bucket. Emails are automatically sent to Assignee(s) for all Action Items.
The 4 buckets functions as follows:
To Do – The Assignor will create the Action Item and reside there until the Assignee receives the email notification and acknowledges receipt to the Assignor via email or text. The Assignor then moves it to In Progress.
In Progress – The Assignee will work on the Action Item and once completed notify the Assignor. The Assignor then moves it to In Review.
In Review – The Assignor will ensure the Action Item is completed and move it to Done, or if not completed, make comments and move it back to In Progress, and subsequently notify the Assignee.
Done – The Assignor moves the Action Item to Done once they are satisfied that it has been fully completed.
Beyond educational purposes, this app provides a platform for creating a customizable and standardized use of the Fishbone Diagram report and Action Item tracking.
This app features:
An overview (Learnings) of the Fishbone Diagram.
A standardized approach and application of the Fishbone Report.
Access to Sensei tips for further understanding.
Access to the Fishbone tool/report to list all possible causes that may have an impact on the effect (problem), which can be color-coded for different user’s input.
Receive awards for completing Action Items, Reports, etc.
Record, assign, and track Action Items with the visual management Kanban board.
View a Fishbone Diagram report displaying all ideas generated.
Create the Fishbone Diagram report and share via PDF.
Take a Quiz to determine knowledge attainment.
Review a video tutorial to assist in understanding how the app works.
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- Paid
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Cluster2A VS. 
Lean Fishbone Diagram RptMarch 17, 2025