KDD 2006 - PDF Preprint Papers

KDD 2006 International Conference on Knowledge Discovery and Data Mining

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KDD
2006
ACM

381views Data Mining» more KDD 2006»

GPLAG: detection of software plagiarism by program dependence graph analysis

14 years 4 months ago

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Along with the blossom of open source projects comes the convenience for software plagiarism. A company, if less self-disciplined, may be tempted to plagiarize some open source pr...

Chao Liu 0001, Chen Chen, Jiawei Han, Philip S. Yu

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Id	Views	Title	Status

1	381	GPLAG: detection of software plagiarism by program dependence graph analysis	claim
2	272	YALE: rapid prototyping for complex data mining tasks	claim
3	253	Adaptive Website Design Using Caching Algorithms	claim
4	240	Adaptive event detection with time-varying poisson processes	claim
5	228	Algorithms for time series knowledge mining	claim
6	222	A component-based framework for knowledge discovery in bioinformatics	claim
7	213	Pragmatic text mining: minimizing human effort to quantify many issues in call logs	claim
8	213	Learning sparse metrics via linear programming	claim
9	208	Frequent subgraph mining in outerplanar graphs	claim
10	201	Polynomial association rules with applications to logistic regression	claim
11	201	Clustering based large margin classification: a scalable approach using SOCP formulation	claim
12	200	A Random-Walk Based Scoring Algorithm Applied to Recommender Engines	claim
13	198	Estimating the global pagerank of web communities	claim
14	198	CFI-Stream: mining closed frequent itemsets in data streams	claim
15	198	Event detection from evolution of click-through data	claim
16	191	Beyond classification and ranking: constrained optimization of the ROI	claim
17	185	How to Define Searching Sessions on Web Search Engines	claim
18	185	Understanding Content Reuse on the Web: Static and Dynamic Analyses	claim
19	183	Discovering interesting patterns through user's interactive feedback	claim
20	181	Cryptographically private support vector machines	claim
21	180	Learning the unified kernel machines for classification	claim
22	179	Group formation in large social networks: membership, growth, and evolution	claim
23	179	Extracting key-substring-group features for text classification	claim
24	177	Topics over time: a non-Markov continuous-time model of topical trends	claim
25	176	A framework for analysis of dynamic social networks	claim
26	175	A mixture model for contextual text mining	claim
27	174	Onboard classifiers for science event detection on a remote sensing spacecraft	claim
28	173	Robust information-theoretic clustering	claim
29	173	BLOSOM: a framework for mining arbitrary boolean expressions	claim
30	172	Attack detection in time series for recommender systems	claim
31	170	Computer aided detection via asymmetric cascade of sparse hyperplane classifiers	claim
32	170	Classification features for attack detection in collaborative recommender systems	claim
33	167	Identifying "best bet" web search results by mining past user behavior	claim
34	167	Clustering pair-wise dissimilarity data into partially ordered sets	claim
35	166	Anonymizing sequential releases	claim
36	165	Outlier detection by sampling with accuracy guarantees	claim
37	165	Training linear SVMs in linear time	claim
38	164	Assessing data mining results via swap randomization	claim
39	164	Sampling from large graphs	claim
40	163	New EM derived from Kullback-Leibler divergence	claim
41	162	Simultaneous record detection and attribute labeling in web data extraction	claim
42	161	Efficient kernel feature extraction for massive data sets	claim
43	160	Coherent closed quasi-clique discovery from large dense graph databases	claim
44	159	Global distance-based segmentation of trajectories	claim
45	157	Using structure indices for efficient approximation of network properties	claim
46	156	Detecting outliers using transduction and statistical testing	claim
47	156	Discovering significant OPSM subspace clusters in massive gene expression data	claim
48	156	Unsupervised learning on k-partite graphs	claim
49	155	Single-pass online learning: performance, voting schemes and online feature selection	claim
50	155	Camouflaged fraud detection in domains with complex relationships	claim
51	153	Spatial scan statistics: approximations and performance study	claim
52	153	Model compression	claim
53	153	Center-piece subgraphs: problem definition and fast solutions	claim
54	153	Semi-supervised time series classification	claim
55	150	Maximally informative k-itemsets and their efficient discovery	claim
56	149	Regularized discriminant analysis for high dimensional, low sample size data	claim
57	147	Summarizing itemset patterns using probabilistic models	claim
58	146	Structure and evolution of online social networks	claim
59	145	Deriving quantitative models for correlation clusters	claim
60	143	Algorithms for discovering bucket orders from data	claim
61	143	Mining for misconfigured machines in grid systems	claim
62	143	Mining long-term search history to improve search accuracy	claim
63	142	New cached-sufficient statistics algorithms for quickly answering statistical questions	claim
64	142	Automatic mining of fruit fly embryo images	claim
65	142	Mining distance-based outliers from large databases in any metric space	claim
66	141	Statistical entity-topic models	claim
67	139	Generating semantic annotations for frequent patterns with context analysis	claim
68	136	Mining quantitative correlated patterns using an information-theoretic approach	claim
69	136	Very sparse random projections	claim
70	134	Learning to rank networked entities	claim
71	134	Is there a grand challenge or X-prize for data mining?	claim
72	134	Identifying bridging rules between conceptual clusters	claim
73	132	Utility-based anonymization using local recoding	claim
74	130	Efficient anonymity-preserving data collection	claim
75	130	Mining relational data through correlation-based multiple view validation	claim
76	130	Discovering significant rules	claim
77	129	Bias and controversy: beyond the statistical deviation	claim
78	129	Suppressing model overfitting in mining concept-drifting data streams	claim
79	128	On privacy preservation against adversarial data mining	claim
80	128	Workload-aware anonymization	claim
81	128	Information extraction, data mining and joint inference	claim
82	127	Recommendation method for extending subscription periods	claim
83	124	CCCS: a top-down associative classifier for imbalanced class distribution	claim
84	123	Mining rank-correlated sets of numerical attributes	claim
85	123	Quantifying trends accurately despite classifier error and class imbalance	claim
86	122	Outlier detection by active learning	claim
87	122	Measuring and extracting proximity in networks	claim
88	122	Tensor-CUR decompositions for tensor-based data	claim
89	121	Query-time entity resolution	claim
90	120	Hierarchical topic segmentation of websites	claim
91	119	Incorporating Concept Hierarchies into Usage Mining Based Recommendations	claim
92	118	Mining for proposal reviewers: lessons learned at the national science foundation	claim
93	118	Reducing the human overhead in text categorization	claim
94	118	Maximum profit mining and its application in software development	claim
95	117	Efficient multidimensional data representations based on multiple correspondence analysis	claim
96	115	Aggregating time partitions	claim
97	115	Supervised probabilistic principal component analysis	claim
98	114	Algorithms for storytelling	claim
99	113	A new efficient probabilistic model for mining labeled ordered trees	claim
100	113	A new multi-view regression approach with an application to customer wallet estimation	claim
101	112	K-means clustering versus validation measures: a data distribution perspective	claim
102	109	Extracting redundancy-aware top-k patterns	claim
103	107	Out-of-core frequent pattern mining on a commodity PC	claim
104	103	A large-scale analysis of query logs for assessing personalization opportunities	claim

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