Algorithms for clustering data
Algorithms for clustering data
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Proceedings of the 6th international conference on Artificial intelligence and law
A method for the development of legal knowledge systems
Proceedings of the 6th international conference on Artificial intelligence and law
Toward knowledge management systems in the legal domain
GROUP '97 Proceedings of the international ACM SIGGROUP conference on Supporting group work: the integration challenge
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
On the merits of building categorization systems by supervised clustering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mutual benefits for AI & law and knowledge management
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
Combining multiple classifiers for text categorization
Proceedings of the tenth international conference on Information and knowledge management
Law in a Digital World
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Evaluation of hierarchical clustering algorithms for document datasets
Proceedings of the eleventh international conference on Information and knowledge management
FREM: fast and robust EM clustering for large data sets
Proceedings of the eleventh international conference on Information and knowledge management
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
On-line EM Algorithm for the Normalized Gaussian Network
Neural Computation
Using Text Segmentation to Enhance the Cluster Hypothesis
AIMSA '08 Proceedings of the 13th international conference on Artificial Intelligence: Methodology, Systems, and Applications
Comparing LDA with pLSI as a dimensionality reduction method in document clustering
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
Legal document clustering with built-in topic segmentation
Proceedings of the 20th ACM international conference on Information and knowledge management
Text mining technique for chinese written judgment of criminal case
PAISI'10 Proceedings of the 2010 Pacific Asia conference on Intelligence and Security Informatics
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
The significance of evaluation in AI and law: a case study re-examining ICAIL proceedings
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Law
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Computational resources for research in legal environments have historically implied remote access to large databases of legal documents such as case law, statutes, law reviews and administrative materials. Today, by contrast, there exists enormous growth in lawyers' electronic work product within these environments, specifically within law firms. Along with this growth has come the need for accelerated knowledge management---automated assistance in organizing, analyzing, retrieving and presenting this content in a useful and distributed manner.In cases where a relevant legal taxonomy is available, together with representative labeled data, automated text classification tools can be applied. In the absence of these resources, document clustering offers an alternative approach to organizing collections, and an adjunct to search.To explore this approach further, we have conducted sets of successively more complex clustering experiments using primary and secondary law documents as well as actual law firm data. Tests were run to determine the efficiency and effectiveness of a number of essential clustering functions. After examining the performance of traditional or hard clustering applications, we investigate soft clustering (multiple cluster assignments) as well as hierarchical clustering. We show how these latter clustering approaches are effective, in terms of both internal and external quality measures, and useful to legal researchers. Moreover, such techniques can ultimately assist in the automatic or semi-automatic generation of taxonomies for subsequent use by classification programs.