Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 3/2015

01.12.2015 | Regular Paper

Mining sequential patterns for classification

verfasst von: Dmitriy Fradkin, Fabian Mörchen

Erschienen in: Knowledge and Information Systems | Ausgabe 3/2015

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

While a number of efficient sequential pattern mining algorithms were developed over the years, they can still take a long time and produce a huge number of patterns, many of which are redundant. These properties are especially frustrating when the goal of pattern mining is to find patterns for use as features in classification problems. In this paper, we describe BIDE-Discriminative, a modification of BIDE that uses class information for direct mining of predictive sequential patterns. We then perform an extensive evaluation on nine real-life datasets of the different ways in which the basic BIDE-Discriminative can be used in real multi-class classification problems, including 1-versus-rest and model-based search tree approaches. The results of our experiments show that 1-versus-rest provides an efficient solution with good classification performance.
Vorheriger Artikel The Mask of ZoRRo: preventing information leakage from documents
Nächster Artikel Evaluating link prediction methods

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
Literatur
1.
Agrawal R, Imielinski T, Swami AN (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international ACM Press, conference on management of data, pp 207–216
2.
Agrawal R, Srikant R (1995) Mining sequential patterns. In: ICDE. IEEE Press, pp 3–14
3.
Asuncion A, Newman D (n.d.) UCI Machine Learning Repository
4.
Batal I, Fradkin D, Harrison J, Moerchen F, Hauskrecht M (2012) Mining recent temporal patterns for event detection in multivariate time series data. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, pp 280–288. doi:10.​1145/​2339530.​2339578
5.
Batal I, Valizadegan H, Cooper GF, Hauskrecht M (2011) A pattern mining approach for classifying multivariate temporal data. In: Proceedings of the 2011 IEEE international conference on bioinformatics and biomedicine, pp 358–365. doi:10.​1109/​BIBM.​2011.​39
6.
Bringmann B, Zimmermann A (2008) One in a million: picking the right patterns. Knowl Inf Syst 18(1):61–81CrossRef
7.
Bringmann B, Zimmermann A, Raedt L, Nijssen S (2006) Dont be afraid of simpler patterns. In: Frnkranz J, Scheffer T, Spiliopoulou M (eds) Knowledge discovery in databases: PKDD 2006, vol 4213 of LNCS. Springer, Berlin, pp 55–66. doi:10.​1007/​11871637_​10
8.
Buza K, Schmidt-Thieme L (2010) Motif-based classification of time series with bayesian networks and svms. In: Fink A, Lausen B, Seidel W, Ultsch A (eds) Advances in data analysis, data handling and business intelligence. Studies in classification, data analysis, and knowledge organization. Springer, Berlin, pp 105–114. doi:10.​1007/​978-3-642-01044-6_​9
9.
Carbonell J, Coldstein J (1998) The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of SIGIR, p 335336
10.
Cheng H, Yan X, Han J, Hsu C-W (2007) Discriminative frequent pattern analysis for effective classification. In: Proceedings of the IEEE ICDE
11.
Cheng H, Yan X, Han J, Yu PS (2008) Direct discriminative pattern mining for effective classification. In: ICDE, pp 169–178
12.
Cover TM, Thomas JA (2006) Elements of information theory, 2nd edn. Wiley, New York
13.
Dong G, Pei J (2007) Sequence data mining. Morgan Kaufmann, BurlingtonMATH
14.
Fan R-E, Chang K-W, Hsieh C-J, Wang X-R, Lin C-J (2008) Liblinear: a library for large linear classification. J Mach Learn Res 9:1871–1874MATH
15.
Fan W, Zhang K, Cheng H, Gao J, Yan X, Han J, Yu PS, Verscheure O (2008) Direct mining of discriminative and essential frequent patterns via model-based search tree. In: KDD, pp 230–238
16.
Fern A (2004) Learning models and formulas of a temporal event logic. PhD thesis, Purdue University, West Lafayette, IN, USA
17.
Fradkin D, Moerchen F (2010) Margin-closed frequent sequential pattern mining. KDD workshop on useful patterns. ACM, New York, NY, USA, pp 45–54
18.
Grahne G, Zhu J (2003) Efficiently using prefix-trees in mining frequent itemsets. In: ICDM workshop on frequent itemset mining implementations
19.
Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATH
20.
Han J, Kamber M (2006) Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann, Burlington
21.
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD international conference on management of data. ACM Press, pp 1–12
22.
Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: SIGMOD, pp 1–12
23.
Ifrim G, Bakir GH, Weikum G (2008) Fast logistic regression for text categorization with variable-length n-grams. In: KDD, pp 354–362
24.
Ifrim G, Wiuf C (2011) Bounded coordinate-descent for biological sequence classification in high dimensional predictor space. In: KDD
25.
Kadous MW (2002) Temporal classification: extending the classification paradigm to multivariate time series. PhD thesis, University of New South Wales
26.
Kerr W, Cohen P, Chang Y-H (2008) Learning and playing in wubble world. In: Proceedings of the fourth artificial intelligence and interactive digital entertainment conference, pp 66–71
27.
Knobbe AJ, Ho EKY (2006) Pattern teams. In: PKDD, pp 577–584
28.
Lee J-G, Han J, Li X, Cheng H (2011) Mining discriminative patterns for classifying trajectories on road networks. IEEE Trans Knowl Data Eng 23(5):713–726CrossRef
29.
Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms. In: Proceedings of the 2003 ACM SIGMOD workshop on research issues in data mining and knowledge discovery. ACM Press, pp 2–11. URL:http://​citeseer.​ist.​psu.​edu/​583097.​html
30.
Lo D, Cheng H, Cia L (2011) Mining closed discriminative dyadic sequential patterns. In: EDBT
31.
Lo D, Han J, Cheng H, Khoo S-C, Sun C (2009) Classification of software behaviros for failure detection: a discriminative pattern mining approach. In: Proceedings of KDD
32.
Lucchese C, Orlando S, Perego R (2006) Fast and memory efficient mining of frequent closed itemsets. IEEE Trans Knowl Data Eng 18(1):21–36CrossRef
33.
Mäntyjärvi J, Himberg J, Kangas P, Tuomela U, Huuskonen P (2004) Sensor signal data set for exploring context recognition of mobile devices. In: Proceedings of PERVASIVE. Springer, pp 18–23
34.
Moerchen F, Thies M, Ultsch A (2011) Efficient mining of all margin-closed itemsets with applications in temporal knowledge discovery and classification by compression. Knowl Inf Syst 29:55–80. doi:10.​1007/​s10115-010-0329-5
35.
Mörchen F, Ultsch A (2005) Optimizing time series discretization for knowledge discovery. In: Proceedings of the ACM SIGKDD. ACM Press, pp 660–665
36.
Mörchen F, Ultsch A (2007) Efficient mining of understandable patterns from multivariate interval time series. Data Min Knowl Discov 15(2):181–215. doi:10.​1007/​s10618-007-0070-1
37.
Morishita S, Sese J (2000) Traversing itemset lattice with statistical metric pruning. In: PODS, pp 226–236
38.
Nijssen S, Kok J (2006) Multi-class correlated pattern mining. In: Bonchi F, Boulicaut J-F (eds) Knowledge discovery in inductive databases, vol 3933 of LNCS. Springer, Berlin, pp 165–187. doi:10.​1007/​11733492_​10
39.
Ohara K, Hara M, Takabayashi K, Motoda H, Washio T (2008) Pruning strategies based on the upper bound of information gain for discriminative subgraph mining. In: PKAW’08, pp 50–60
40.
Papaterou P, Kollios G, Sclaroff S, Gunopoulos D (2005) Discovering frequent arrangements of temporal intervals. In: ICDM, pp 354–361
41.
Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu M-C (2001) PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. In: Proceedings of the IEEE ICDE. IEEE Press, pp 215–224
42.
Sese J, Morishita S (2004) Itemset classified clustering, In: Boulicaut J-F, Esposito F, Giannotti F, Pedreschi D (eds) Knowledge discovery in databases: PKDD 2004, vol 3202 of LNCS. Springer, Berlin, pp 398–409. doi:10.​1007/​978-3-540-30116-5_​37
43.
Sipos R, Fradkin D, Moerchen F, Wang Z (2014) Log-based predictive maintenance, In: Proceedings of the 20th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1867–1876. doi:10.​1145/​2623330.​2623340
44.
45.
Starner T, Weaver J, Pentland A (1998) Real-time American sign language recognition using desk and wearable computer-based video. IEEE Trans Pattern Anal Mach Intell 20(12):1371–1375. doi:10.​1109/​34.​735811
46.
Wang J, Han J (2004) BIDE: Efficient mining of frequent closed sequences. In: ICDE. IEEE Press, pp 79–90
47.
Wang J, Han J, Li C (2007) Frequent closed sequence mining without candidate maintenance. IEEE Trans Knowl Data Eng 19(8):1042–1056MathSciNetCrossRef
48.
Wu S-Y, Chen Y-L (2007) Mining nonambiguous temporal patterns for interval-based events. IEEE Trans Knowl Data Eng 19(6):742–758CrossRef
49.
Xu W, Huang L, Fox A, Patterson D, Jordan M (2008) Mining console logs for large-scale system problem detection. In: Proceedings of the 3rd workshop on tackling computer systems problems with machine learning techniques
50.
Yan X, Han J (2002) gspan: Graph-based substructure pattern mining. In: ICDM
51.
Yang Y, Pedersen J (1997) A comparative study on feature selection in text categorization. In: ICML, pp 412–420
52.
Zaki M (2001) Spade: an efficient algorithm for mining frequent sequences. Mach Learn 42:31–60CrossRefMATH
53.
Zaki MJ, Hsiao C-J (2002) CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of the 2nd SIAM international conference on data mining (SDM), SIAM, pp 457–473
Metadaten
Titel
Mining sequential patterns for classification
verfasst von
Dmitriy Fradkin
Fabian Mörchen
Publikationsdatum
01.12.2015
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 3/2015
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-014-0817-0

Weitere Artikel der Ausgabe 3/2015

Knowledge and Information Systems 3/2015 Zur Ausgabe

Regular Paper

On the anonymizability of graphs

Regular Paper

Multiplicative distance: a method to alleviate distance instability for high-dimensional data

Regular Paper

Optimal WCDMA network planning by multiobjective evolutionary algorithm with problem-specific genetic operation

Regular Paper

Estimating robustness in large social graphs

Regular Paper

Evaluating link prediction methods

Survey Paper

A survey on data stream clustering and classification