Frequent Pattern Mining System

A Frequent Pattern Mining System is a Pattern Mining System that can solve a Frequent-Pattern Mining Task by implementing a Frequent-Pattern Mining Algorithm.

• AKA: Frequent-Pattern Recognition System, Frequent-Pattern Learning System.
• Context:
  • It ranges from being a Supervised Frequent-Pattern Mining System to being an Unsupervised Frequent-Pattern Mining System.
  • It can range from being a Frequent Itemset Mining System, to being a Frequent Structured Pattern Mining System, to being a Frequent Sequential Pattern Mining System.
  • …
• Example(s):
  • an Associative Classification System,
  • a Constraint-Based Mining System,
  • a Correlation Mining System,
  • a Frequent Structured Pattern Mining System such as:
    • a Frequent Trees Pattern Mining System,
    • a Frequent Graphs Pattern Mining System,
  • a Frequent-Itemset Mining System such as:
    • an Association Rule Mining System;
    • a Frequent Itemset Summarization System (e.g. Yan et al., 2005),
  • a Frequent Pattern-based Clustering,
  • a Frequent Sequential Pattern Mining System.
  • …
• Counter-Example(s):
  • an Emerging Pattern Mining System,
  • a Relational Pattern Mining System,
  • a Sequential Pattern Mining System‎.
• See: Pattern Recognition System, Knowledge Discovery, Correlation, Exploratory Data Analysis, Graph Pattern Mining System, Structured Pattern Mining System.

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Frequent_pattern_discovery Retrieved:2019-10-4.
  • Frequent pattern discovery (FP discovery, FP mining, or Frequent itemset mining) as part of knowledge discovery in databases / Massive Online Analysis, and data mining describes the task of finding the most frequent and relevant patterns in large datasets. . The concept was first introduced for mining transaction databases.Frequent patterns are defined as subsets (itemsets, subsequences, or substructures) that appear in a data set with frequency no less than a user-specified or auto-determined threshold.

2016

• (Yan, 2016) ⇒ Xifeng Yan (2016). "Frequent Pattern Mining". In: KDD Topics 2016.
  • QUOTE: 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 . A few text books are available on this topic, e.g., .

2007

• (Han et al., 2007) ⇒ Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. (2007). “Frequent Pattern Mining: current status and future directions.” In: Data Mining and Knowledge Discovery, 15(1). doi:10.1007/s10618-006-0059-1
  • Frequent pattern mining has been a focused theme in data mining research for over a decade. 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.

2006

• (Yin, Han & Yu, 2006) ⇒ Xiaoxin Yin, Jiawei Han, and Philip S. Yu. (2006). “LinkClus: efficient clustering via heterogeneous semantic links.” In: Proceedings of the 32nd International Conference on Very large data bases (VLDB 2006).
  • QUOTE: The problem of finding groups of nodes with high tightness can be reduced to the problem of finding frequent patterns (Agrawal, Imieliński & Swami, 1993). A tight group is a set of nodes that are co-linked with many objects of other types, just like a frequent pattern is a set of items that co-appear in many transactions. ...
• (Horváth et al., 2006) ⇒ Tamás Horváth, Jan Ramon, and Stefan Wrobel. (2006). “Frequent Subgraph Mining in Outerplanar Graphs.” In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/1150402.1150427
  • QUOTE: In recent years there has been an increased interest in algorithms that can perform frequent pattern discovery in large databases of graph structured objects.

2005

• (Yan et al., 2005) ⇒ Xifeng Yan, Hong Cheng, Jiawei Han, and Dong Xin. (2005). "Summarizing Itemset Patterns: a profile-based approach". In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. doi:10.1145/1081870.1081907

2004

• (Han et al., 2004) ⇒ Jiawei Han, Jian Pei, Yiwen Yin, and Runying Mao. (2004). “Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach.” In: Journal Data Mining and Knowledge Discovery, 8(1). doi:10.1023/B:DAMI.0000005258.31418.83

1993

• (Agrawal, Imieliński & Swami, 1993) ⇒ Rakesh Agrawal, Tomasz Imieliński, and Arun Swami. (1993). “Mining Association Rules Between Sets of Items in Large Databases.” In: Proceedings of ACM SIGMOD Conference (SIGMOD 1993). do>10.1145/170035.170072.

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