Sequence Clustering Algorithm
The Sequence Clustering algorithm is a data mining algorithm used in SQL Server Analysis Services (SSAS) to discover patterns in sequences of events. It groups similar sequences together based on their characteristics, allowing for deeper insights into customer behavior, transaction histories, and other time-dependent data.
Overview
This algorithm is particularly useful for scenarios where the order of events matters. For example:
- Analyzing website navigation paths to understand user journeys.
- Identifying common sequences of product purchases.
- Detecting patterns in medical treatment histories.
- Understanding the sequence of customer service interactions.
The Sequence Clustering algorithm partitions a set of sequences into distinct clusters. Each cluster represents a group of sequences that share common properties or exhibit similar behaviors.
How it Works
The algorithm typically involves the following steps:
- Sequence Representation: Input data is structured into sequences, where each sequence is an ordered list of events.
- Feature Extraction: Relevant features are extracted from the sequences, such as the types of events, their durations, and their order.
- Clustering: A clustering technique, often based on distance metrics or probability models, is applied to group similar sequences into clusters.
- Cluster Profiling: Each discovered cluster is analyzed and profiled to understand its defining characteristics and the typical sequences it contains.
Key Concepts
- Sequences: An ordered series of events.
- Events: Individual items or actions within a sequence.
- Clusters: Groups of similar sequences.
- Attributes: The data points that describe each event in a sequence.
Parameters
The Sequence Clustering algorithm in SSAS offers several configurable parameters to fine-tune its behavior:
- CLUSTER_COUNT: Specifies the desired number of clusters.
- MAX_ITERATIONS: Sets the maximum number of iterations for the clustering process.
- MIN_SUPPORT: Defines the minimum number of sequences required for an event pattern to be considered significant.
- DISTANCE_THRESHOLD: A parameter used to control the similarity between sequences when forming clusters.
Using the Algorithm in SSAS
To use the Sequence Clustering algorithm in SQL Server Analysis Services:
- Create a new Data Mining project in SQL Server Data Tools (SSDT).
- Configure a Data Source and Data Source View that contains your sequence data.
- Create a new Mining Structure and select the Sequence Clustering algorithm.
- Define the structure of your sequence data, identifying the sequence identifier, the case table, and the content/nested tables that represent events.
- Train the mining model using your data.
- Explore and analyze the discovered clusters using the Sequence Cluster viewer in SSDT.
Example Scenario
Consider a retail scenario where you want to understand customer purchasing behavior. You have transactional data that includes customer ID, transaction date, and products purchased. By transforming this data into sequences of products purchased by each customer over time, you can use the Sequence Clustering algorithm to identify groups of customers with similar buying patterns. This can inform targeted marketing campaigns and product recommendations.
Note:
The Sequence Clustering algorithm requires careful data preparation. Ensure your data is structured correctly with a clear sequence identifier and ordered events.