LIST OF TABLES Parameters used in analyzing FIM-CQTransSWin Characteristics of Mushroom and Retail Datasets 99
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1 LIST OF TABLES Table Title Page No. 3.1 Item Equivalent Number Binary & Decimal Equivalents of transactions Candidate 1-itemset, C Frequent 1-itemset, F Candidate 2-itemset, C Candidate 3-itemset, C Frequent itemsets in CQTransSWin Binary & Decimal Equivalents of transactions in sample data stream C 1 of CQTimeSWin F 1 of CQTimeSWin C 2 of CQTimeSWin F 2 of CQTimeSWin All Frequent itemsets in CQTimeSWin C 1 of CQTimeSWin F 1 of CQTimeSWin C 2 of CQTimeSWin F 2 of CQTimeSWin All Frequent itemsets in CQTimeSWin Parameters used in analyzing FIM-CQTransSWin Characteristics of Mushroom and Retail Datasets Parameters used in analyzing FIM-CQTimeSWin Number of MFIs, CFIs, and FIs CIs and their Keys CI-Table Superset-Table Subset-Table Transaction-Table for { T 1, T 2, T 3, T 4 } 122 xi
2 4.7 CI-Table for { T 1, T 2, T 3, T 4 } Data stream with 5 transactions Portion of Data Stream, D containing five transactions Features of Datasets NCSA Common Log File Entry Directives in W3C Extended Log Format ALS Field Description HTTP Response Status Codes Number of FIs Retrieved 192 xii
3 LIST OF FIGURES Figure Title Page No. 1.1 Data Stream Mining Simple architecture for an online auction monitoring system Landmark Window Model Damped Window Model Sliding Window Model Weighted Sliding Window Model Algorithm Insert Algorithm Delete Sliding Window of size N Sliding Window, CQTransSWin Sliding Window, CQTransSWin Initialization of sliding window Sliding After T 4 arrives Algorithm FIM-CQTransSWin CQTimeSWin 1 and CQTimeSWin Algorithm FIM-CQTimeSWin Execution Time Vs Window Size Execution Time Vs Threshold Execution Time Vs Number of Items Execution Time Vs Number of Transactions Initialization Time Vs Support - Mushroom dataset Sliding Time Vs Support - Mushroom dataset FI Generation Time Vs Support - Mushroom dataset Initialization Time Vs Window Size - Mushroom dataset Sliding Time Vs Window size - Mushroom dataset FI Generation Time Vs Window Size - Mushroom dataset Initialization Time Vs Support - Retail dataset Sliding Time Vs Support - Retail dataset FI Generation Time Vs Support - Retail dataset Initialization Time Vs Window Size - Retail dataset Sliding Time Vs Window size - Retail dataset 105 xiii
4 3.26 FI Generation Time Vs Window Size - Retail dataset Relationships between FI, CFI and MFI Architecture of HATCI Algorithm Algorithm AddNewTransaction Algorithm RemoveOldestTransaction Algorithm Generate-CFI State of HATCI tables after <T 1, cd> State of HATCI tables after <T 2, ab> Updated HATCI tables after <T 3, abc> Updated HATCI tables after <T 4, abc> Updated HATCI tables after removing <T 1, cd> Updated HATCI tables after <T 5, bc> Number of FIs Vs CFIs Chess dataset Number of FIs Vs CFIs Mushroom dataset No. of CFIs retrieved by MOMENT Vs HATCI Algorithms Chess dataset No. of CFIs retrieved by MOMENT Vs HATCI Algorithms Mushroom dataset Computational time of MOMENT and HATCI Algorithms Chess dataset Computational time of MOMENT and HATCI Algorithms Mushroom dataset Procedure Insert Procedure Inorder_Traversal Algorithm FOCIT Algorithm Build_ITree Algorithm Update Algorithm Generate FOCIT after T FOCIT after T FOCIT after T FOCIT after T FOCIT after T FOCIT after T xiv
5 5.13 A Sample Binary Search Tree Execution Time Time for Updating CIs Execution Time Vs Threshold Chess Dataset Execution Time Vs Threshold Mushroom Dataset Execution Time Vs Threshold Retail Dataset Categorization of Web Mining Data Mining Techniques Applied to Web Log Data Preprocessing of Web Log Files Loading Web Log File in WEKA Removal of Irrelevant Attributes Reducing Dataset with RemoveRange Filter Reduced Web Log File Numeric to Nominal Conversion Removing Instances with Status Code other than Sample Preprocessed Web Log File Parameter Setting for Apriori Algorithm Top-10 Association Rules CFIs retrieved from Web Log File using HATCI algorithm 196 xv
6 LIST OF ABBREVIATIONS AND SYMBOLS ABBREVIATIONS A - Close - Apriori-based Close AFPCFI-DS - An improved FP tree based algorithm for Closed Frequent Itemset Mining over Data Streams ALS - Analytic Logging Service API - Application Programming Interface AR - Association Rules ARFF - Attribute Relation File Format ASCII - American Standard Code for Information Interchange ATM - Automated Teller Machine AVL Tree - Adelson Velskii and Landis Tree BST - Binary Search Tree BTS - Buffer, Trie and SetGen BV-List - Bit Vector List BVTable - Bit Vector Table CET - Closed Enumeration Trees CFI - Closed Frequent Itemset CHARM - Closed Association Rule Mining CI - Closed Itemset CI Table - Closed Itemset Table CIL - Closed Itemset Lattice CILattice - Closed Itemset Lattice CI-Tree - Closed Itemset Tree CL- Stream - Concept Lattice Stream CLICI - Concept Lattice based Incremental Closed Itemset CLOSET - CLOsed itemset CloStream - Closed Frequent Itemsets over Data Stream xvi
7 CPU - Central Processing Unit CQTimeSWin - Circular Queue based Time Sensitive Sliding Window CQTransSWin - Circular Queue based Transaction Sensitive Sliding Window CSV - Comma Separated Value DBCA - Dynamic Base Combinatorial Approximation DBMS - DataBase Management Systems. DBV - Dynamic Bit Vectors DCI - Closed - Direct Count & Intersect Closed itemset DHP - Direct Hashing and Pruning DIC - Dynamic Itemset Counting DSM - FI - Data Stream Mining for Frequent Itemsets E - Commerce - Electronic Commerce ECLAT - Equivalence CLAss Transformation EDI - Electronic Data Interchange EMAFCI - Efficient Mining Algorithm for Frequent Closed Itemsets estdec - estimating Recent Frequent Itemsets based on Decay Rate estwin - estimating Recent Frequent Itemsets in a sliding Window F - Fahrenheit FCI - Frequent Closed Itemset FI - Frequent Itemset FIFO - First In First Out FIM- CQTransSWin - Frequent Itemset Mining using Circular Queue based Transaction Sensitive Sliding Window FIM-CQimeSWin - Frequent Itemset Mining using Circular Queue based Time Sensitive Sliding Window xvii
8 FOCIT - FOrest of Closed Itemset Trees FP- growth - Frequent Pattern Growth FP- tree - Frequent Pattern Tree FP-CDS - FP-tree based Closed itemsets mining from Data Stream FPCFI DS - FP tree-based algorithm for Closed Frequent Itemset Mining over Data Streams FPClose - Frequent Pattern based Closed Itemset mining FUP - Fast Update GCT - Global Closed frequent itemset Tree GenMax - Generate Maximal Frequent Itemset GFI - Great Frequent Itemset GGACFI-MFW - Generating Global Approximate Closed Frequent Itemset on Max Frequency Window model GMT - Greenwich Mean Time GNU - GNU's Not Unix GPS - Global Positioning System H- Mine - Hyper structure Mine HATCI - HAsh Table of Closed Itemsets hminer - Hash Based Miner HTC - Hash Table of Closed Itemsets HTML - Hypertext Markup Language HTTP - Hyper Text Transfer Protocol I/O - Input/ Output IC3 - Internet Crime Complaint Center ID - Identifier IIS - Internet Information Server Index FCI - Index based Frequent Closed Itemset mining xviii
9 INSTANT - maximal frequent So-far itemset maintainer IP - Internet Protocol ISP - Internet Service Provider ITree - Intersection Tree JSP - Java Server Pages LDS - List based Data Stream Mining MAFIA - MAximal Frequent Itemset Algorithm Max Miner - Maximal Frequent Itemset Miner Max-FISM - Maximal Frequent ItemSets Mining MFI - Maximal Frequent Itemset MFI TransSW - Mining Frequent Itemsets within a Transaction sensitive Sliding Window MFI-TimeSW - Mining Frequent Itemsets within a Time sensitive Sliding Window MFP Tree - Max-Frequency Pattern tree MiFI - Mining Frequent Itemsets minsup - Minimum Support MOMENT - Maintaining Closed Frequent Itemsets by Incremental Updates MSNBC - Microsoft and National Broadcasting Company NCSA - National Center for Supercomputing Applications PDA - Personal Digital Assistant RFID - Radio Frequency Identification SABMA - Systolic Array Based Mining Algorithm SC - Support Count SFI - Forest - Summary Frequent Itemset Forest S-List - Support List SWCA - Sliding Window based Combinatorial Approximation xix
10 SWF - Sliding Window Filtering TCET - Transaction translate Closed Enumeration Tree TCP - Transmission Control Protocol TDS - Transaction Data Stream TFP - Top-K FCI using FP-Tree TID - Transaction Identifier TKC-DS - Top-K frequent Closed Frequent itemsets of Data Streams TMoment - Transaction- Moment ToDoFIS - Top Down Frequent Itemset Search Top- K- FCI - Top- K Frequent Closed Itemsets TU - Time Unit URL - Uniform Resource Locator VSW - Variable Size Sliding Window W3C - World Wide Web Consortium WAS - WebSphere Application Server WEKA - Waikato Environment for Knowledge Analysis WSN - Wireless Sensor Networks WSW - Weighted Sliding Window XML - Extensible Markup Language xx
11 SYMBOLS - Subset of or equal to - Superset of or equal to - Element of - Comes Before - Summation α - Alpha β - Beta - Subset of - Superset of ℇ - Epsilon σ - Sigma - Almost Equal to θ - Theta δ - Delta << - Left Shift >> - Right shift - Intersection - Union χ - Chi < - Less than <= - Less than or equal to > - Greater than >= - Greater than or equal to - For All xxi
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