Frequent Tree Mining System

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

• AKA: Frequent-Pattern Tree Mining System, Frequent Trees Pattern Mining System, Frequent-Pattern Tree Learning System, Frequent-Pattern Tree Recognition System.
• Context:
  • It can range from being a Frequent Ordered Tree Mining System to being an Frequent Unordered Tree Mining System.
  • It can range from being a Frequent Rooted Tree Mining System to being an Frequent Unrooted Tree Mining System.
  • …
• Example(s):
  • A Frequent Subtree Mining System such as:
    • a Chopper System;
    • a CMTreeMiner System;
    • a DRYADE System;
    • a FreeTreeMiner System;
    • a FREQT System;
    • a HybridTreeMiner System;
    • a PathJoin System;
    • a Phylominer System;
    • a POTMiner System;
    • a SLEUTH System;
    • a TreeMiner System;
    • a uFreqtSystem;
    • a Unot System;
    • a Xspanner System;
    • a EvoMiner System,
  • A Frequent-Pattern Growth Tree Mining System,
• Counter-Example(s):
  • a Constraint-Based Mining System,
  • an Emerging Pattern Mining System,
  • a Frequent Subgraph Finding System,
  • an Frequent Itemset Mining System,
  • a Frequent Pattern-based Clustering System,
  • a Frequent Sequence Mining System,
  • a Relational Pattern Recognition System.
• See: Pattern Recognition System, Association Rule Learning System, Apriori Algorithm, Tree Structure.

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References

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., .

2014

• (Deepak et al., 2014) ⇒ Akshay Deepak, David Fernández-Baca, Srikanta Tirthapura, Michael J. Sanderson, and Michelle M. Mcmahon. (2014)."EvoMiner: Frequent Subtree Mining in Phylogenetic Databases".; In: Knowledge and Information Systems Journal, 41(3). doi:10.1007/s10115-013-0676-0

2004a

• (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

2004b

• (Chi et al., 2004) ⇒ Yun Chi, Richard R. Muntz, Siegfried Nijssen, and Joost N. Kok. (2001, 2004).

2002

• (Zaki, 2002) ⇒ Mohammed J. Zaki. (2002). “Efficiently Mining Frequent Trees in a Forest.” In: Proceedings of the eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. doi:10.1145/775047.775058
  • QUOTE: Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semi-structured data, and so on.

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