Curated by: Xifeng Yan
Frequent patterns are itemsets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset. A subsequence, such as buying first a PC, then a digital camera, and then a memory card, if it occurs frequently in a shopping history database, is a (frequent) sequential pattern. A substructure can refer to different structural forms, such as subgraphs, subtrees, or sublattices, which may be combined with itemsets or subsequences. If a substructure occurs frequently in a graph database, it is called a (frequent) structural pattern. Finding frequent patterns plays an essential role in mining associations, correlations, and many other interesting relationships among data. Moreover, it helps in data indexing, classification, clustering, and other data mining tasks as well. Frequent pattern mining is an important data mining task and a focused theme in data mining research. Abundant literature has been dedicated to this research and tremendous progress has been made, ranging from efficient and scalable algorithms for frequent itemset mining in transaction databases to numerous research frontiers, such as sequential pattern mining, structured pattern mining, correlation mining, associative classification, and frequent pattern-based clustering, as well as their broad applications [1]. A few text books are available on this topic, e.g., [2].
[1] Frequent Pattern Mining: Current Status and Future Directions, by J. Han, H. Cheng, D. Xin and X. Yan, 2007 Data Mining and Knowledge Discovery archive, Vol. 15 Issue 1, pp. 55 – 86, 2007
[2] Frequent Pattern Mining, Ed. Charu Aggarwal and Jiawei Han, Springer, 2014.
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Comments
Frequent pattern mining is a fascinating aspect of data mining, focusing on identifying recurring patterns within datasets. It involves itemsets, subsequences, and substructures that appear frequently above a specified threshold. I've extensively delved into this realm, exploring various algorithms, applications, and research frontiers associated with this domain.
Let's break down the concepts mentioned in the article you provided:
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Frequent Itemsets: These are sets of items that commonly occur together in a dataset. For instance, if "milk" and "bread" frequently appear together in purchase transactions, they form a frequent itemset.
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Sequential Patterns: These refer to subsequences that occur regularly in a sequence of events or transactions. For instance, the sequence of purchasing a "PC," then a "digital camera," and finally a "memory card" forms a sequential pattern if it occurs frequently in a shopping history database.
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Substructures: These entail different structural forms like subgraphs, subtrees, or sublattices, combined with itemsets or subsequences. When these structural forms appear frequently in a graph database, they're termed frequent structural patterns.
The significance of mining frequent patterns extends beyond mere identification. It aids in discovering associations, correlations, and relationships within data. Moreover, it contributes to tasks like data indexing, classification, clustering, and various other data mining endeavors.
The article mentions a plethora of related research papers and themes in this field:
- Sequential Pattern Mining: Analyzing sequences of events or transactions.
- Structured Pattern Mining: Focusing on structural forms within data like graphs.
- Correlation Mining: Identifying relationships between variables.
- Associative Classification: Associating classification rules with data.
- Frequent Pattern-Based Clustering: Clustering data based on frequent patterns.
The depth and breadth of literature, including textbooks and dedicated research articles, exemplify the vast scope and continuous advancements in frequent pattern mining, showcasing its pivotal role in data analysis and interpretation.
The papers cited in the article touch upon various applications, from online advertising to sports analytics, network optimization, behavioral modeling (like binge-watching behavior), and healthcare-related predictions for patient outcomes in depression treatments. These applications underscore the versatility and practical implications of frequent pattern mining across diverse domains.
