Grid-based Data Mining For Market Basket
Analysis In The Retail Sector
Author(s)
R. P. Singh, A. Turi & D. Malerba
Abstract
Recent advances in computing, communications, and digital storage
technologies, together with the development of high-throughput data-acquisition
technologies, have made it possible to gather and store incredible volumes of
data. The warehouses of international retailers (such as Wal-Mart) are typically
multi-terabyte databases that contain information about retail transactions by
customers all over the world. The emergence of these large data sets creates a
growing need for analyzing them across geographical lines using distributed and
parallel systems like the Grid infrastructure, thereby unlocking the intelligence
hidden deep within these geographically distributed databases. Market basket
analysis is a method for discovering consumer purchasing patterns by extracting
associations or co-occurrences from the stores’ transaction database. This is a
typical association rule mining task where an Apriori algorithm is widely
adopted to find out the large item-set. But since the traditional sequential Apriori
algorithm can no longer serve the purpose due to the huge amount of data, the
strategy for a parallel and distributed association rule mining algorithm is
outlined in this paper.
Keywords: grid, data mining, market basket analysis, retail sector.
1 Introduction
The data that businesses collect about their customers is one of their greatest
assets. Buried within this vast amount of data is valuable information that could
make a significant difference to the way in which any business organization run
their business, interact with their current and prospective customers and gain the
competitive edge on their competitors. Market basket analysis applies
Keywords
grid, data mining, market basket analysis, retail sector.
Related Book
Other papers in this volume
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