Density peaks clustering (DPC) algorithm published in the US journal Science in 2014 is a novel clustering algorithm based on density. It needs neither iterative process nor more parameters. However, original algorithm only has taken into account the global structure of data, which leads to missing many clusters. In addition, DPC does not perform well when data sets have relatively high dimension. Especially, DPC generates wrong number of clusters of real-world data sets. In order to overcome the first problem, we propose a density peaks clustering based on k nearest neighbors (DPC-KNN) which introduces the idea of k nearest neighbors (KNN) into DPC and has another option for the local density computation. In order to overcome the second problem, we introduce principal component analysis (PCA) into the model of DPC-KNN and further bring forward a method based on PCA (DPC-KNN-PCA), which preprocesses high-dimensional data. By experiments on synthetic data sets, we demonstrate the feasibility of our algorithms. By experiments on real-world data sets, we compared this algorithm with k-means algorithm and spectral clustering (SC) algorithm in accuracy. Experimental results show that our algorithms are feasible and effective.

Study on density peaks clustering based on k-nearest neighbors and principal component analysis

A local density-based approach for outlier detection

The ring signature allows a signer to leak secrets anonymously, without the risk of identity escrow. At the same time, the ring signature provides great flexibility: No group manager, no special setup, and the dynamics of group choice. The ring signature is, however, vulnerable to malicious or irresponsible signers in some applications, because of its anonymity. In this paper, we propose a traceable ring signature scheme. A traceable ring scheme is a ring signature except that it can restrict "excessive" anonymity. The traceable ring signature has a tag that consists of a list of ring members and an issue that refers to, for instance, a social affair or an election. A ring member can make any signed but anonymous opinion regarding the issue, but only once (per tag). If the member submits another signed opinion, possibly pretending to be another person who supports the first opinion, the identity of the member is immediately revealed. If the member submits the same opinion, for instance, voting "yes" regarding the same issue twice, everyone can see that these two are linked. The traceable ring signature can suit to many applications, such as an anonymous voting on a BBS. We formalize the security definitions for this primitive and show an efficient and simple construction in the random oracle model.

/pdf/traceable-ring-signature-1fwoarlgfm.pdf

Traceable ring signature

https://estudogeral.sib.uc.pt/bitstream/10316/5042/1/filec983b44ba0b8489db5983985ef05dfd7.pdf

Improving hierarchical cluster analysis: A new method with outlier detection and automatic clustering

We propose two multi-label learning approaches with LIFT reduction.The idea of fuzzy rough set attribute reduction is adopted in our approaches.Sample selection improves the efficiency in feature dimension reduction. In multi-label learning, since different labels may have some distinct characteristics of their own, multi-label learning approach with label-specific features named LIFT has been proposed. However, the construction of label-specific features may encounter the increasing of feature dimensionalities and a large amount of redundant information exists in feature space. To alleviate this problem, a multi-label learning approach FRS-LIFT is proposed, which can implement label-specific feature reduction with fuzzy rough set. Furthermore, with the idea of sample selection, another multi-label learning approach FRS-SS-LIFT is also presented, which effectively reduces the computational complexity in label-specific feature reduction. Experimental results on 10 real-world multi-label data sets show that, our methods can not only reduce the dimensionality of label-specific features when compared with LIFT, but also achieve satisfactory performance among some popular multi-label learning approaches.

Multi-label learning with label-specific feature reduction

This paper describes a system for the animation of algorithms on an electronic chalkboard. The instructor teaching an algorithm can enter data directly through a drawing – the algorithm then makes use of this data, for example numbers, or the image of a graph. The drawing becomes alive. The result is a more natural way of teaching and starting algorithmic animations. The paper also describes how to couple a sign and handwriting recognition engine with the animation system. The lecturer can then write programs using her own handwriting, and the programs runs. All animations can be enriched with sound and explanations from the lecturer and can be posted to the Web.

Advances in Web-Based Learning – ICWL 2004

Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining

Advances in Web-Based Learning - Icwl 2010

This book constitutes the refereed proceedings of the 8th International Conference on Web-Based Learning, ICWL 2009, held in Aachen, Germany, in August 2009. The 38 revised full papers and 14 short papers are presented together with three invited papers and were carefully reviewed and selected from 106 submissions. They deal with topics such as technology enhanced learning, web-based learning for oriental languages, mobile learning, social software and Web 2.0 for technology enhanced learning, learning resource deployment, organization and management, design, model and framework of E-learning systems, e-learning metadata and standards, educational gaming and multimedia storytelling for learning, as well as practice and experience sharing and pedagogical Issues

Advances in Web Based Learning ICWL 2009 : 8th International Conference, Aachen, Germany, August 19-21, 2009. Proceedings

Keynote Presentations.- Incompleteness in Data Mining.- Mining E-Commerce Data: The Good, the Bad, and the Ugly.- Seamless Integration of Data Mining with DBMS and Applications.- Web Mining.- Applying Pattern Mining to Web Information Extraction.- Empirical Study of Recommender Systems Using Linear Classifiers.- iJADE eMiner - A Web-Based Mining Agent Based on Intelligent Java Agent Development Environment (iJADE) on Internet Shopping.- A Characterized Rating Recommend System.- Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents.- Text Mining.- Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification.- Predictive Self-Organizing Networks for Text Categorization.- Meta-learning Models for Automatic Textual Document Categorization.- Efficient Algorithms for Concept Space Construction.- Topic Detection, Tracking, and Trend Analysis Using Self-Organizing Neural Networks.- Automatic Hypertext Construction through a Text Mining Approach by Self-Organizing Maps.- Applications and Tools.- Semantic Expectation-Based Causation Knowledge Extraction: A Study on Hong Kong Stock Movement Analysis.- A Toolbox Approach to Flexible and Efficient Data Mining.- Determining Progression in Glaucoma Using Visual Fields.- Seabreeze Prediction Using Bayesian Networks.- Semi-supervised Learning in Medical Image Database.- On Application of Rough Data Mining Methods to Automatic Construction of Student Models.- Concept Hierarchies.- Concept Approximation in Concept Lattice.- Generating Concept Hierarchies/Networks: Mining Additional Semantics in Relational Data.- Representing Large Concept Hierarchies Using Lattice Data Structure.- Feature Selection.- Feature Selection for Temporal Health Records.- Boosting the Performance of Nearest Neighbour Methods with Feature Selection.- Feature Selection for Meta-learning.- Interestingness.- Efficient Mining of Niches and Set Routines.- Evaluation of Interestingness Measures for Ranking Discovered Knowledge.- Peculiarity Oriented Mining and Its Application for Knowledge Discovery in Amino-Acid Data.- Sequence Mining.- Mining Sequence Patterns from Wind Tunnel Experimental Data for Flight Control.- Scalable Hierarchical Clustering Method for Sequences of Categorical Values.- FFS - An I/O-Efficient Algorithm for Mining Frequent Sequences.- Sequential Index Structure for Content-Based Retrieval.- Spatial and Temporal Mining.- The S 2-Tree: An Index Structure for Subsequence Matching of Spatial Objects.- Temporal Data Mining Using Hidden Markov-Local Polynomial Models.- Patterns Discovery Based on Time-Series Decomposition.- Criteria on Proximity Graphs for Boundary Extraction and Spatial Clustering.- Micro Similarity Queries in Time Series Database.- Association Mining.- Mining Optimal Class Association Rule Set.- Generating Frequent Patterns with the Frequent Pattern List.- User-Defined Association Mining.- Direct and Incremental Computing of Maximal Covering Rules.- Towards Efficient Data Re-mining (DRM).- Data Allocation Algorithm for Parallel Association Rule Discovery.- Classification and Rule Induction.- Direct Domain Knowledge Inclusion in the PA3 Rule Induction Algorithm.- Hierarchical Classification of Documents with Error Control.- An Efficient Data Compression Approach to the Classification Task.- Combining the Strength of Pattern Frequency and Distance for Classification.- A Scalable Algorithm for Rule Post-pruning of Large Decision Trees.- Optimizing the Induction of Alternating Decision Trees.- Building Behaviour Knowledge Space to Make Classification Decision.- Clustering.- Efficient Hierarchical Clustering Algorithms Using Partially Overlapping Partitions.- A Rough Set-Based Clustering Method with Modification of Equivalence Relations.- Importance of Individual Variables in the k-Means Algorithm.- A Hybrid Approach to Clustering in Very Large Databases.- Advanced Topics and New Methods.- A Similarity Indexing Method for the Data Warehousing - Bit-Wise Indexing Method.- Rule Reduction over Numerical Attributes in Decision Trees Using Multilayer Perceptron.- Knowledge Acquisition from Both Human Expert and Data.- Neighborhood Dependencies for Prediction.- Learning Bayesian Networks with Hidden Variables Using the Combination of EM and Evolutionary Algorithms.- Interactive Construction of Decision Trees.- An Improved Learning Algorithm for Augmented Naive Bayes.- Generalised RBF Networks Trained Using an IBL Algorithm for Mining Symbolic Data.

Advances in knowledge discovery and data mining : 5th Pacific-Asia Conference, PAKDD 2001, Hong Kong, China, April 16-18, 2001 : proceedings

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