Time Series Clustering: A Superior Alternative for Market Basket Analysis
Time Series Clustering: A Superior Alternative for Market Basket Analysis
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DOI：
10.1007/978-981-4585-18-7_28
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发表时间：
2013
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影响因子：
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通讯作者：
Swee Chuan Tan;Jess Pei San Lau
中科院分区：
文献类型：
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作者：
Swee Chuan Tan;Jess Pei San Lau
市场篮子分析往往涉及到在海量销售交易数据上应用事实上的关联规则挖掘方法。在本文中，我们认为关联规则挖掘并不总是最适合分析大市篮子数据的方法。这是因为用于关联规则挖掘的数据矩阵通常很大且稀疏，导致许多缺乏洞察力的琐碎规则的生成速度很慢。为了解决这个问题，我们将一个真实世界的销售交易数据集汇总为时间序列格式。然后，我们使用时间序列聚类来发现对定价或制定交叉销售策略有用的常用购买项目。我们表明，这种方法使用的数据集比用于关联分析的数据小得多。此外，它还揭示了在使用关联分析时难以发现的重要模式和见解。
Market Basket Analysis often involves applying the de facto association rule mining method on massive sales transaction data. In this paper, we argue that association rule mining is not always the most suitable method for analysing big market-basket data. This is because the data matrix to be used for association rule mining is usually large and sparse, resulting in sluggish generation of many trivial rules with little insight. To address this problem, we summarise a real-world sales transaction data set into time series format. We then use time series clustering to discover commonly purchased items that are useful for pricing or formulating cross-selling strategies. We show that this approach uses a data set that is substantially smaller than the data to be used for association analysis. In addition, it reveals significant patterns and insights that are otherwise hard to uncover when using association analysis.