A fast algorithm for building lattices
Information Processing Letters
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Algorithms for Mining Distance-Based Outliers in Large Datasets
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
An effective and efficient algorithm for high-dimensional outlier detection
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental classification rules based on association rules using formal concept analysis
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
A novel attribute reduction approach based on the object oriented concept lattice
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
A completeness analysis of frequent weighted concept lattices and their algebraic properties
Data & Knowledge Engineering
| Hi-index | 0.10 |
Traditional outlier mining methods identify outliers from a global point of view. It is usually difficult to find deviated data points in low-dimensional subspaces using these methods. The concept lattice, due to its straight-forwardness, conciseness and completeness in knowledge expression, has become an effective tool for data analysis and knowledge discovery. In this paper, a concept lattice based outlier mining algorithm (CLOM) for low-dimensional subspaces is proposed, which treats the intent of every concept lattice node as a subspace. First, sparsity and density coefficients, which measure outliers in low-dimensional subspaces, are defined and discussed. Second, the intent of a concept lattice node is regarded as a subspace, and sparsity subspaces are identified based on a predefined sparsity coefficient threshold. At this stage, whether the intent of any ancestor node of a sparsity subspace is a density subspace is identified based on a predefined density coefficient threshold. If it is a density subspace, then the objects in the extent of the node whose intent is a sparsity subspace are defined as outliers. Experimental results on a star spectral database show that CLOM is effective in mining outliers in low-dimensional subspaces. The accuracy of the results is also greatly improved.