A Simple Dynamic Mind-map Framework To Discover Associative Relationships in Transactional Data Streams

TL;DR

This paper introduces a dynamic mind-map framework for real-time discovery of associative relationships in transactional data streams, enabling adaptive and incremental analysis without fixed architecture constraints.

Contribution

It presents a novel dynamic, connectionist structure that processes data streams incrementally, allowing recursive and adaptive association discovery in real-time.

Findings

Enables on-demand association analysis in data streams.

Supports recursive and adaptive structure processing.

Avoids fixed-size architecture limitations.

Abstract

In this paper, we informally introduce dynamic mind-maps that represent a new approach on the basis of a dynamic construction of connectionist structures during the processing of a data stream. This allows the representation and processing of recursively defined structures and avoids the problem of a more traditional, fixed-size architecture with the processing of input structures of unknown size. For a data stream analysis with association discovery, the incremental analysis of data leads to results on demand. Here, we describe a framework that uses symbolic cells to calculate associations based on transactional data streams as it exists in e.g. bibliographic databases. We follow a natural paradigm of applying simple operations on cells yielding on a mind-map structure that adapts over time.