Knowledge Graph and Semantic Computing: Knowledge Graph Empowers the Digital Economy
7th China Conference, CCKS 2022, Qinhuangdao, China, August 24–27, 2022, Revised Selected Papers
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Article
USBE: User-similarity based estimator for multimedia cold-start recommendation
To address user cold-start challenge in multimedia recommender systems, we proposed a new model named USBE in this paper. The model doesn’t take the new user’s personal and social information as the necessary ...
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Article
An automatic algorithm for software vulnerability classification based on CNN and GRU
In order to improve the management efficiency of software vulnerability classification, reduce the risk of system being attacked and destroyed, and save the cost for vulnerability repair, this paper proposes a...
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Chapter and Conference Paper
Identifying Influential Spreaders in Complex Networks Based on Degree Centrality
Identifying the most influential spreaders in complex networks is vital for optimally using the network structure and accelerating information diffusion. In most previous methods, the edges are treated equally...
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Book and Conference Proceedings
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Article
Near-surface PM2.5 prediction combining the complex network characterization and graph convolution neural network
Massive studies focus on the prediction of main pollutants, to improve air quality by revealing the evolution of pollutants. However, existing prediction methods mostly emphasize the fitting analysis of time s...
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Chapter and Conference Paper
Research on Software Community Division Method Based on Inter-node Dependency
The mining of software community structure is of great significance in identifying software design pattern, software maintenance, software security and optimizing software structure. To improve the accuracy of...
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Article
Uncertain maximal frequent subgraph mining algorithm based on adjacency matrix and weight
How to mine many interesting subgraphs in uncertain graph has become an important research field in data mining. In this paper, a novel algorithm Uncertain Maximal Frequent Subgraph Mining Algorithm Based on A...
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Chapter and Conference Paper
Interactive Multi-model Target Maneuver Tracking Method Based on the Adaptive Probability Correction
Non-cooperative target tracking is a key technology for complex space missions such as on-orbit service. To improve the tracking performance during the unknown maneuvering phase of the target, two methods incl...
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Article
Sequence clustering algorithm based on weighted vector identification
Sequence clustering has become an important topic that experts in data mining are currently investigating. However, clustering quality is typically significantly affected by both the selection of initial cent...
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Article
Representative points clustering algorithm based on density factor and relevant degree
Most of the existing clustering algorithms are affected seriously by noise data and high cost of time. In this paper, on the basis of CURE algorithm, a representative points clustering algorithm based on densi...
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Article
Open AccessA Phase Change Memory Chip Based on TiSbTe Alloy in 40-nm Standard CMOS Technology
In this letter, a phase change random access memory (PCRAM) chip based on Ti0.4Sb2Te3 alloy material was fabricated in a 40-nm 4-metal level complementary metal-oxide semiconductor (CMOS) technology. The phase ch...
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Article
Mining sequential patterns with periodic wildcard gaps
Mining frequent patterns with periodic wildcard gaps is a critical data mining problem to deal with complex real-world problems. This problem can be described as follows: given a subject sequence, a pre-specif...
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Chapter and Conference Paper
Incremental Mining Algorithm of Sequential Patterns Based on Sequence Tree
At present, the existing incremental mining algorithms of sequential patterns can not make full use of the mining information in original database, the updated database is scanned many times and the projected ...
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Chapter and Conference Paper
Frequent Itemset Mining Based on Bit-Sequence
Most existing frequent itemset mining algorithms based on bit-sequence will generate many candidate itemsets. In this paper, we present FIM-BS for mining frequent itemset. First we adopt bit-sequence to compre...
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Chapter and Conference Paper
Hierarchical K-Means Clustering Algorithm Based on Silhouette and Entropy
Hierarchical K-means clustering is one of important clustering task in data mining. In order to address the problem that the time complexity of the existing HK algorithms is high and most of algorithms are sen...
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Chapter and Conference Paper
A Clustering Algorithm Based on Matrix over High Dimensional Data Stream
Clustering high-dimensional data stream is a difficult and important issue. In this paper, we propose MStream, a new clustering algorithm based on matrix over high dimensional data stream. MStream algorithm in...
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Chapter and Conference Paper
A Method for Analyzing Software Faults Based on Mining Outliers’ Feature Attribute Sets
Faults analysis is a hot topic in the field software security. In this paper, the concepts of the improved Euclidian distance and the feature attribute set are defined. A novel algorithm MOFASIED for mining ou...
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Chapter and Conference Paper
Software Fault Feature Clustering Algorithm Based on Sequence Pattern
Software fault feature analysis has been the important part of software security property analysis and modeling. In this paper, a software fault feature clustering algorithm based on sequence pattern (SFFCSP) ...
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Chapter and Conference Paper
MMFI_DSSW – A New Method to Incrementally Mine Maximal Frequent Itemsets in Transaction Sensitive Sliding Window
Due to streaming data are infinite in length and fast changing with time, it is very significant to limit the memory usage in the process of mining data streams. Maximal frequent itemset is a subset of frequen...
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Chapter and Conference Paper
Index-Based Load Shedding for Streaming Sliding Window Joins
We present a novel clustering-based indexing load shedding technique for sliding window joins (CILS). When the details of the distribution of streams are unknown, to obtain the statistics of data, we dynamical...
