ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function

[1]  Kim-Kwang Raymond Choo,et al.  A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns , 2018, Soft Comput..

[2]  Shadi Aljawarneh,et al.  G-SPAMINE: An approach to discover temporal association patterns and trends in internet of things , 2017, Future Gener. Comput. Syst..

[3]  Gugulothu Narsimha,et al.  CLAPP: A self constructing feature clustering approach for anomaly detection , 2017, Future Gener. Comput. Syst..

[4]  Carson K. Leung,et al.  A new framework for mining weighted periodic patterns in time series databases , 2017, Expert Syst. Appl..

[5]  Shadi Aljawarneh,et al.  A new agent approach for recognizing research trends in wearable systems , 2017, Comput. Electr. Eng..

[6]  Shadi Aljawarneh,et al.  Anomaly-based intrusion detection system through feature selection analysis and building hybrid efficient model , 2017, J. Comput. Sci..

[7]  Shadi Aljawarneh,et al.  A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining , 2017, Future Gener. Comput. Syst..

[8]  Shadi Aljawarneh,et al.  A computationally efficient approach for temporal pattern mining in IoT , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[9]  V. Radhakrishna,et al.  Estimating temporal pattern bounds using negative support computations , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[10]  Vangipuram Radhakrishna,et al.  Looking into the possibility of novel dissimilarity measure to discover similarity profiled temporal association patterns in IoT , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[11]  Shadi Aljawarneh,et al.  A similarity measure for outlier detection in timestamped temporal databases , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[12]  G. Narsimha,et al.  Design of novel fuzzy distribution function for dimensionality reduction and intrusion detection , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[13]  Shadi A. Aljawarneh,et al.  A similarity measure for temporal pattern discovery in time series data generated by IoT , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[14]  Vangipuram Radhakrishna,et al.  A computationally optimal approach for extracting similar temporal patterns , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[15]  Vangipuram Radhakrishna,et al.  Mining of outlier temporal patterns , 2016, 2016 International Conference on Engineering & MIS (ICEMIS).

[16]  Shadi Aljawarneh,et al.  Investigations of automatic methods for detecting the polymorphic worms signatures , 2016, Future Gener. Comput. Syst..

[17]  Vangipuram Radhakrishna,et al.  Estimating Prevalence Bounds of Temporal Association Patterns to Discover Temporally Similar Patterns , 2016 .

[18]  Vangipuram Radhakrishna,et al.  A Computationally Efficient Approach for Mining Similar Temporal Patterns , 2016 .

[19]  Tzung-Pei Hong,et al.  Mining fuzzy temporal association rules by item lifespans , 2016, Appl. Soft Comput..

[20]  Suh-Yin Lee,et al.  Mining Temporal Patterns in Time Interval-Based Data , 2015, IEEE Transactions on Knowledge and Data Engineering.

[21]  Vangipuram Radhakrishna,et al.  A Novel Approach for Mining Similarity Profiled Temporal Association Patterns Using Venn Diagrams , 2015, ArXiv.

[22]  Gunupudi Rajesh Kumar,et al.  An improved k-Means Clustering algorithm for Intrusion Detection using Gaussian function , 2015 .

[23]  Vangipuram Radhakrishna,et al.  A Survey on Temporal Databases and Data mining , 2015 .

[24]  Shie-Jue Lee,et al.  A Similarity Measure for Text Classification and Clustering , 2014, IEEE Transactions on Knowledge and Data Engineering.

[25]  Andrew K. C. Wong,et al.  Discovery of Temporal Associations in Multivariate Time Series , 2014, IEEE Transactions on Knowledge and Data Engineering.

[26]  Roque Marín,et al.  Mining generalized temporal patterns based on fuzzy counting , 2013, Expert Syst. Appl..

[27]  Roque Marín,et al.  A tree structure for event-based sequence mining , 2012, Knowl. Based Syst..

[28]  Ahmad Abdollahzadeh Barforoush,et al.  Efficient colossal pattern mining in high dimensional datasets , 2012, Knowl. Based Syst..

[29]  Shie-Jue Lee,et al.  A Fuzzy Self-Constructing Feature Clustering Algorithm for Text Classification , 2011, IEEE Transactions on Knowledge and Data Engineering.

[30]  Ajith Abraham,et al.  An efficient algorithm for incremental mining of temporal association rules , 2010, Data Knowl. Eng..

[31]  Shashi Shekhar,et al.  Similarity-Profiled Temporal Association Mining , 2009, IEEE Transactions on Knowledge and Data Engineering.

[32]  Chia-Wen Chang,et al.  Fast discovery of sequential patterns in large databases using effective time-indexing , 2008, Inf. Sci..

[33]  Shashi Shekhar,et al.  Mining Temporal Association Patterns under a Similarity Constraint , 2008, SSDBM.

[34]  John F. Roddick,et al.  ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data , 2007, Data Knowl. Eng..

[35]  Philip S. Yu,et al.  Mining Colossal Frequent Patterns by Core Pattern Fusion , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[36]  Susan P. Imberman,et al.  Discovery of Association Rules in Temporal Databases , 2007, Fourth International Conference on Information Technology (ITNG'07).

[37]  Gösta Grahne,et al.  Fast algorithms for frequent itemset mining using FP-trees , 2005, IEEE Transactions on Knowledge and Data Engineering.

[38]  Christian Borgelt,et al.  Keeping things simple: finding frequent item sets by recursive elimination , 2005 .

[39]  Shashi Shekhar,et al.  Mining Time-Profiled Associations: An Extended Abstract , 2005, PAKDD.

[40]  Wan-Jui Lee,et al.  An efficient algorithm to discover calendar-based temporal association rules , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[41]  Wan-Jui Lee,et al.  Discovery of fuzzy temporal association rules , 2004, IEEE Trans. Syst. Man Cybern. Part B.

[42]  Mohammed J. Zaki,et al.  Fast vertical mining using diffsets , 2003, KDD '03.

[43]  Ming-Syan Chen,et al.  Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules , 2003, IEEE Trans. Knowl. Data Eng..

[44]  Suh-Yin Lee,et al.  Fast Discovery of Sequential Patterns by Memory Indexing , 2002, DaWaK.

[45]  Ming-Syan Chen,et al.  Sliding-window filtering: an efficient algorithm for incremental mining , 2001, CIKM '01.

[46]  Cheng Yang,et al.  Efficient discovery of error-tolerant frequent itemsets in high dimensions , 2001, KDD '01.

[47]  Sushil Jajodia,et al.  Discovering calendar-based temporal association rules , 2001, Proceedings Eighth International Symposium on Temporal Representation and Reasoning. TIME 2001.

[48]  Umeshwar Dayal,et al.  PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth , 2001, ICDE 2001.

[49]  Anthony K. H. Tung,et al.  Constraint-based clustering in large databases , 2001, ICDT.

[50]  Mohammed J. Zaki Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[51]  Gustavo Rossi,et al.  An approach to discovering temporal association rules , 2000, SAC '00.

[52]  Xiaodong Chen,et al.  Discovering Temporal Association Rules: Algorithms, Language and System , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[53]  Edith Cohen,et al.  Finding interesting associations without support pruning , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[54]  Kien A. Hua,et al.  Mining Interval Time Series , 1999, DaWaK.

[55]  Wynne Hsu,et al.  Mining association rules with multiple minimum supports , 1999, KDD '99.

[56]  Jinyan Li,et al.  Efficient mining of emerging patterns: discovering trends and differences , 1999, KDD '99.

[57]  Jiawei Han,et al.  Efficient mining of partial periodic patterns in time series database , 1999, Proceedings 15th International Conference on Data Engineering (Cat. No.99CB36337).

[58]  Sridhar Ramaswamy,et al.  On the Discovery of Interesting Patterns in Association Rules , 1998, VLDB.

[59]  X.S. Wang,et al.  Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences , 1998, IEEE Trans. Knowl. Data Eng..

[60]  Sridhar Ramaswamy,et al.  Cyclic association rules , 1998, Proceedings 14th International Conference on Data Engineering.

[61]  Sushil Jajodia,et al.  Testing complex temporal relationships involving multiple granularities and its application to data mining (extended abstract) , 1996, PODS.

[62]  Ramakrishnan Srikant,et al.  Mining quantitative association rules in large relational tables , 1996, SIGMOD '96.

[63]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[64]  Jiawei Han,et al.  Maintenance of discovered association rules in large databases: an incremental updating technique , 1996, Proceedings of the Twelfth International Conference on Data Engineering.

[65]  Ramakrishnan Srikant,et al.  Mining generalized association rules , 1995, Future Gener. Comput. Syst..

[66]  Jiawei Han,et al.  Discovery of Multiple-Level Association Rules from Large Databases , 1995, VLDB.

[67]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[68]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[69]  Tomasz Imielinski,et al.  Database Mining: A Performance Perspective , 1993, IEEE Trans. Knowl. Data Eng..

[70]  William G. Marchal,et al.  Statistical techniques in business and economics , 1991 .

[71]  Vangipuram RADHAKRISHNA,et al.  Normal Distribution Based Similarity Profiled Temporal Association Pattern Mining (N-SPAMINE) , 2017 .

[72]  Vangipuram Radhakrishna,et al.  An Approach for Mining Similar Temporal Association Patterns in Single Database Scan , 2016 .

[73]  Vangipuram Radhakrishna,et al.  A Novel Similar Temporal System Call Pattern Mining for Efficient Intrusion Detection , 2016, J. Univers. Comput. Sci..

[74]  Asif Imran,et al.  Web Data Amalgamation for Security Engineering: Digital Forensic Investigation of Open Source Cloud , 2016, J. Univers. Comput. Sci..

[75]  Gugulothu Narsimha,et al.  An Approach for Intrusion Detection Using Novel Gaussian Based Kernel Function , 2016, J. Univers. Comput. Sci..

[76]  Das Amrita,et al.  Mining Association Rules between Sets of Items in Large Databases , 2013 .

[77]  J. S. Yoo Temporal Data Mining: Similarity-Profiled Association Pattern , 2012 .

[78]  Keun Ho Ryu,et al.  Mining temporal interval relational rules from temporal data , 2009, J. Syst. Softw..

[79]  Shikha Gupta,et al.  Mining Frequent Closed Itemsets for Association Rules , 2009 .

[80]  J. Pei,et al.  Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach , 2006, Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06).

[81]  Toon Calders,et al.  Axiomatization of frequent itemsets , 2003, Theor. Comput. Sci..

[82]  Fei Wu,et al.  Knowledge discovery in time-series databases , 2001 .

[83]  Rakesh Agrawal,et al.  Parallel Mining of Association Rules: Design, Implementation and Experience , 1999 .

[84]  Sushil Jajodia,et al.  Mining Temporal Relationships with Multiple Granularities in Time Sequences , 1998, IEEE Data Eng. Bull..

[85]  Philip S. Yu,et al.  Mining association rules with adjustable accuracy , 1997, CIKM '97.

[86]  Heikki Mannila,et al.  Discovering Frequent Episodes in Sequences , 1995, KDD.