Mining Frequent Itemsets (MFI) over Data Streams: Variable Window Size (VWS) by Context Variation Analysis (CVA) of the Streaming Transactions

TL;DR

This paper introduces MFI-VWS-CVA, a novel method for mining frequent itemsets over data streams that adaptively adjusts window size based on context variation, reducing memory use and computational costs.

Contribution

The paper presents a new variable window size technique guided by context variation analysis for efficient, scalable frequent itemset mining in data streams.

Findings

The proposed algorithm outperforms existing methods in efficiency.

Adaptive window sizing improves detection of concept drift.

Memory utilization is significantly reduced.

Abstract

The challenges with respect to mining frequent items over data streaming engaging variable window size and low memory space are addressed in this research paper. To check the varying point of context change in streaming transaction we have developed a window structure which will be in two levels and supports in fixing the window size instantly and controls the heterogeneities and assures homogeneities among transactions added to the window. To minimize the memory utilization, computational cost and improve the process scalability, this design will allow fixing the coverage or support at window level. Here in this document, an incremental mining of frequent item-sets from the window and a context variation analysis approach are being introduced. The complete technology that we are presenting in this document is named as Mining Frequent Item-sets using Variable Window Size fixed by Context Variation Analysis (MFI-VWS-CVA). There are clear boundaries among frequent and infrequent item-sets in specific item-sets. In this design we have used window size change to represent the conceptual drift in an information stream. As it were, whenever there is a problem in setting window size effectively the item-set will be infrequent. The experiments that we have executed and documented proved that the algorithm that we have designed is much efficient than that of existing.