In this paper, an accurate Sequential-Patterns based Classifier, called SPaC-NF, is proposed. SPaC-NF introduces a new pruning strategy, using the Netconf as measure of interest, that allows to prune the rules search space for building specific rules with high Netconf. Additionally, a new strategy for generating the Sequential-Patterns based Rules and a new way of ordering them based on their rule sizes and Netconf values is introduced in SPaC-NF. The ordering strategy together with the ``Best K rules'' satisfaction mechanism allows SPaC-NF to have better accuracy than SVM, J48, NaiveBayes and PART classifiers. Most of these classifiers were evaluated using Weka, a popular suite of machine learning software. The experiments were done using ten-fold cross-validation, reporting the average over the ten folds. Similar to other works, experiments were conducted using several document collections, three in our case: AFP, TDT and Reuter.
AgrawalR., MannilaH., SrikantR., ToivonenH. and VerkamoA.I., Fast discovery of association rules, in: Advances in Knowledge Discovery and Data Mining, FayyadU. and et al., eds, 1996.
2.
AgrawalR. and SrikantR., Fast algorithms for mining association rules in large databases, in: VLDB'94, Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, 1994, Santiago de Chile, Chile, (1994), 487-499.
3.
AgrawalR. and SrikantR., Mining sequential patterns, in: Proceedings of the Eleventh International Conference on Data Engineering, ICDE '95, IEEE Computer Society, Washington, DC, USA, (1995), 3-14.
4.
AhnK.I. and KimJ.Y., Efficient mining of frequent itemsets and a measure of interest for association rule mining, Information and Knowledge Management3(3) (2004), 245-257.
5.
BaralisE. and GarzaP., A lazy approach to pruning classification rules, in: Proceedings of the ICDM, (2002), 35-42.
6.
BerzalF., CuberoJ.C., SánchezD. and SerranoJ.M., ART: A hybrid classification model, Mach Learn54(1) (2004), 67-92.
7.
BreimanL., FriedmanJ.H., OlshenR.A. and StoneC.J., Classification and Regression Trees, Wadsworth International Group, Belmont, CA, 1984.
8.
BrinS., MotwaniR., UllmanJ.D. and TsurS., Dynamic itemset counting and implication rules for market basket data, SIGMOD Rec26(2) (1997).
9.
BuddeewongS. and KreesuradejW., A new association rule-based text classifier algorithm, in: Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence, ICTAI '05, IEEE Computer Society, Washington, DC, USA, (2005), 684-685.
10.
CesarioE., FolinoF., LocaneA., MancoG. and OrtaleR., Boosting text segmentation via progressive classification, Knowl Inf Syst15(3) (2008), 285-320.
11.
Chia-YingH., Don-LinY. and JungpinW., An efficient sequential pattern mining algorithm based on the 2-sequence matrix, in: IEEE International Conference on Data Mining Workshops, (2008), 583-591.
12.
CoenenF. and LengP., An evaluation of approaches to classification rule selection, in: Proceedings of the Fourth IEEE International Conference on Data Mining, (2004), 359-362.
13.
CoenenF., LengP.H. and LuZ., Threshold tuning for improved classification association rule mining, volume 3518 of Lecture Notes in Computer Science, Springer, (2005), 216-225.
14.
CortesC. and VapnikV., Support-vector networks, Mach Learn20(3) (1995), 273-297.
15.
DemsarJ., Statistical comparisons of classifiers over multiple data sets, Journal of Machine Learning Research7 (2006), 1-30.
16.
DeogunJ.S. and JiangL., Prediction mining - an approach to mining association rules for prediction, in: 10th International Conference, RSFDGrC, volume 3642 of Lecture Notes in Computer Science, Springer, (2005), 98-108.
17.
ExarchosT.P., PapaloukasC., LamprosC. and FotiadisD.I., Protein classification using sequential pattern mining, Conf Proc IEEE Eng Med Biol Soc (2006).
18.
ExarchosT.P., TsipourasM.G., PapaloukasC. and FotiadisD.I., A two-stage methodology for sequence classification based on sequential pattern mining and optimization, Data Knowl Eng66(3) (2008), 467-487.
19.
ExarchosT.P., TsipourasM.G., PapaloukasC. and FotiadisD.I., An optimized sequential pattern matching methodology for sequence classification, Knowl Inf Syst19(2) (2009), 249-264.
20.
FaghihiU., Fournier-VigerP., NkambouR. and PoirierP., A generic episodic learning model implemented in a cognitive agent by means of temporal pattern mining, volume 5579 of Lecture Notes in Computer Science, Springer, (2009), 545-555.
21.
Fournier-VigerP., FaghihiU., NkambouR. and NguifoE.M., CMRULES: An efficient algorithm for mining sequential rules common to several sequences, in: Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference, May 19-21, 2010, Daytona Beach, Florida, AAAI Press, (2010).
22.
Fournier-VigerP., NkambouR. and TsengV.S.-M., RuleGrowth: Mining sequential rules common to several sequences by pattern-growth, in: Proceedings of the 2011 ACM Symposium on Applied Computing (SAC), TaiChung, Taiwan, March 21-24, 2011, ACM, (2011), 956-961.
23.
FrankE. and WittenI.H., Generating accurate rule sets without global optimization, in: Fifteenth International Conference on Machine Learning, ShavlikJ., ed., Morgan Kaufmann, 1998, pp. 144-151.
24.
GalianoF.B., BlancoI.J., SánchezD. and MirandaM.A.V., Measuring the accuracy and interest of association rules: A new framework, Intell Data Anal6(3) (2002), 221-235.
25.
GarcíaS. and HerreraF., An extension on statistical comparisons of classifiers over multiple data sets for all pairwise comparisons, Journal of Machine Learning Research9 (2009), 2677-2694.
26.
García-HernándezR.A., Martínez-TrinidadJ.F. and Carrasco-OchoaJ.A., A fast algorithm to find all the maximal frequent sequences in a text, volume 3287 of Lecture Notes in Computer Science, Springer, (2004), 478-486.
27.
GarofalakisM., RastogiR. and ShimK., SPIRIT: Sequential pattern mining with regular expression constraints, in: Proceedings of the 25th International Conference on Very Large Data Bases, (1999), 223-234.
28.
GoudaK., HassaanM. and ZakiM.J., Prism: An effective approach for frequent sequence mining via prime-block encoding, J Comput Syst Sci76(1) (2010), 88-102.
29.
HaleemH., KumarP. and BegS., Novel frequent sequential patterns based probabilistic model for effective classification of web documents, in: Computer and Communication Technology (ICCCT), 2014 International Conference on, (2014), 361-371.
30.
HallM., FrankE., HolmesG., PfahringerB., ReutemannP. and WittenI.H., The WEKA data mining software: An update, SIGKDD Explor Newsl11(1) (2009), 10-18.
31.
HamiltonH.J. and KarimiK., The TIMERS II algorithm for the discovery of causality, in: PAKDD, (2005), 744-750.
32.
Hernández-LeónR., Carrasco-OchoaJ.A., Martínez-TrinidadJ.F. and Hernández-PalancarJ., Combining hybrid rule ordering strategies based on netconf and a novel satisfaction mechanism for CAR-based classifiers, Intell Data Anal18(6S) (2014), S89-S100.
33.
Hernández-LeónR., Carrasco-OchoaJ.A., Martínez-TrinidadJ.F. and PalancarJ.H., CAR-NF: A classifier based on specific rules with high netconf, Intell Data Anal16(1) (2012), 49-68.
34.
HoltJ.D. and ChungS.M., Multipass algorithms for mining association rules in text databases, Knowl Inf Syst3(2) (2001), 168-183.
35.
JailletS., LaurentA. and TeisseireM., Sequential patterns for text categorization, Intell Data Anal10(3) (2006), 199-214.
36.
LeshN., ZakiM.J. and OgiharaM., Mining features for sequence classification, in: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (1999), 342-346.
37.
LiC., YangQ., WangJ. and LiM., Efficient mining of gap-constrained subsequences and its various applications, ACM Trans Knowl Discov Data6(1), 1-39.
38.
LiW., HanJ. and PeiJ., CMAR: Accurate and efficient classification based on multiple class-association rules, in: Proceedings of the IEEE International Conference on Data Mining, (2001), 369-376.
39.
LiuB., HsuW. and MaY., Integrating classification and association rule mining, in: proceedings of the 4th International Conference on Knowledge Discovery and Data Mining, (1998), 80-86.
40.
LoD., KhooS.-C. and WongL., Non-redundant sequential rules - Theory and algorithm, Inf Syst34(4-5) (2009), 438-453.
41.
MannilaH., ToivonenH. and VerkamoA.I., Discovery of frequent episodes in event sequences, Data Min Knowl Discov1(3) (1997), 259-289.
42.
ParkS.-H., ReyesJ.A., GilbertD.R., KimJ.W. and KimS., Prediction of protein-protein interaction types using association rule based classification.
43.
PeiJ., HanJ., Mortazavi-aslB., PintoH., ChenQ., DayalU. and HsuM., PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, in: Proceedings of the 17th International Conference on Data Engineering, (2001), 215-224.
44.
PipanmaekapornL. and LiY., A pattern discovery model for effective text mining, in: Machine Learning and Data Mining in Pattern Recognition - 8th International Conference.
45.
QuinlanJ.R., C45: Programs for Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993.
46.
ShettarR., Sequential pattern mining from web log data, International Journal of Engineering Science and Advanced Technology2 (2012), 204-208.
47.
SilversteinC., BrinS. and MotwaniR., Beyond market baskets: Generalizing association rules to dependence rules, Data Min Knowl Discov2(1) (1998), 39-68.
48.
SrikantR. and AgrawalR., Mining sequential patterns: Generalizations and performance improvements, in: Proceeding in the 5th International Conference Extending Database Technology, (1996), 3-17.
49.
SteinbachM. and KumarV., Generalizing the notion of confidence, in: ICDM, IEEE Computer Society (2005), 402-409.
50.
TeredesaiA.M., AhmadM.A., KanodiaJ. and GaborskiR.S., CoMMA: A framework for integrated multimedia mining using multi-relational associations, Knowl Inf Syst10(2) (2006), 135-162.
51.
TsaiC.Y., ChenC.J. and ChienC.J., A time-interval sequence classification method, Knowl Inf Syst (2012), 1-28.
52.
WangW., WangY.J., Banares-AlcántaraR., CuiZ. and CoenenF., Application of classification association rule mining for mammalian mesenchymal stem cell differentiation, in: Advances in Data Mining Applications and Theoretical Aspects, 9th Industrial Conference, PernerP., ed.
53.
WangY., XinQ. and CoenenF., A novel rule ordering approach in classification association rule mining, Lecture Notes in Computer Science (2007), 339-348.
54.
WangY.J., XinQ. and CoenenF., A novel rule weighting approach in classification association rule mining, in: ICDM Workshops, IEEE Computer Society (2007), 271-276.
55.
WangY.J., XinQ. and CoenenF., Hybrid rule ordering in classification association rule mining, Trans MLDM1(1) (2008), 1-15.
56.
XeiF., WuX. and ZhuX., Document-specific keyphrase extraction using sequential patterns with wildcards, in: Proceedings of the IEEE 14th International Conference on Data Mining, Shenzhen, China, (2014).
57.
YangD.L., HsiehY.-L. and WuJ., Using data mining to study upstream and downstream causal relationship in stock market, in: JCIS, Atlantis Press, 2006.
58.
YangZ., WangY. and KitsuregawaM., LAPIN: Effective sequential pattern mining algorithms by last position induction for dense databases, in: DASFAA, RamamohanaraoK., KrishnaP.R., MohaniaM.K. and NantajeewarawatE., eds, volume 4443 of Lecture Notes in Computer Science, Springer, (2007), 1020-1023.
59.
YoonY. and LeeG.G., Practical application of associative classifier for document classification, in: Proceedings of the Second Asia Conference on Asia Information Retrieval Technology, AIRS'05, Springer-Verlag, Berlin, Heidelberg, (2005), 467-478.
60.
YuanM., OuyangY. and XiongZ., A text categorization method using extended vector space model by frequent term sets, J Inf Sci Eng29(1) (2013), 99-114.
61.
ZakiM.J., SPADE: An efficient algorithm for mining frequent sequences, Machine Learning Journal42 (2001), 31-60.
62.
ZakiM.J., ParthasarathyS., OgiharaM. and LiW., New algorithms for fast discovery of association rules, in: In 3rd Intl Conf on Knowledge Discovery and Data Mining, AAAI Press, (1997), 283-286.
