Fuzzy Clustering for Streaming Environment with Explainable Parameter Determination

Document Type

Book Chapter

Publication Title

Explainable Interpretable and Transparent AI Systems

Abstract

The importance of real-time analysis is growing, and the explainability of learning about the environment derived from data is also an important trait for understanding and application. Due to the dynamic, continuous, and drifting nature of streaming data, every learning model should be capable of discerning the properties of the environment in real-time with adaptability. Due to the concept drift and the emergence of new concepts that are very common in a streaming environment, fuzzy clustering will be advantageous for consideration, as it provides the feature where each data point will have a dedicated member for each cluster, and due to the drifting nature of the environment, this membership will play a critical role in making the clustering algorithm adaptive. The proposed algorithm determines the number of clusters and the fuzzifier value depending on the data entropy. The membership of each data point changes as concepts drift, and the algorithm detects this; similarly, as new clusters emerge, it detects new clusters and determines the membership of each data point. The algorithm’s novelty is the detection of several clusters through learning with data and adaptability to the environment. The experimental analysis demonstrates the proposed method’s efficacy on real-world and benchmark synthetic data.

First Page

177

Last Page

202

DOI

10.1201/9781003442509-11

Publication Date

1-1-2024

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