@inproceedings{uyen2009,
author = {Uyen T. V. Nguyen and Laurence A. F. Park and Liang Wang and Kotagiri Ramamohanarao},
booktitle = {AI 2009: Advances in Artificial Intelligence},
date-added = {2009-09-10 12:27:48 +0200},
date-modified = {2009-09-10 12:29:06 +0200},
doi = {10.1007/978-3-642-10439-8_29},
issn = {0302-9743},
keywords = {laurence},
month = {December},
pages = {280-290},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
title = {A Novel Path-based Clustering Algorithm Using Multi-dimensional Scaling},
volume = {5866},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/path-based.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-10439-8_29}
}
@inproceedings{ravana2009,
address = {New York, USA},
author = {Sri Ravana and Laurence A.~F.~Park and Alistair Moffat},
booktitle = {Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval},
date-added = {2009-04-22 09:51:44 +1000},
date-modified = {2009-09-10 12:44:08 +0200},
doi = {10.1145/1571941.1572129},
editor = {Mark Sanderson and ChengXiang Zhai and Justin Zobel and James Allan and Javed Aslam},
keywords = {laurence},
month = {July},
pages = {788-789},
publisher = {ACM Press},
title = {System Scoring Using Partial Prior Information},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/system-scoring-2009.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1145/1571941.1572129}
}
@inproceedings{webber2009,
address = {New York, USA},
author = {William Webber and Laurence A.~F.~Park},
booktitle = {Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval},
date-added = {2009-02-17 14:06:31 +1100},
date-modified = {2009-09-10 12:42:47 +0200},
doi = {10.1145/1571941.1572018},
editor = {Mark Sanderson and ChengXiang Zhai and Justin Zobel and James Allan and Javed Aslam},
keywords = {laurence},
month = {July},
pages = {444-451},
publisher = {ACM Press},
title = {Score Adjustment for Correction of Pooling Bias},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/pooling-bias-2009.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1145/1571941.1572018}
}
@inproceedings{guo2008.4,
author = {Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence A.~F.~Park},
booktitle = {Advances in Knowledge Discovery and Data Mining},
date-added = {2008-10-24 09:32:41 +1100},
date-modified = {2009-02-11 13:48:50 +1100},
doi = {10.1007/978-3-642-01307-2_77},
keywords = {laurence},
month = {April},
pages = {757-763},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
title = {Grouped {ECOC} Conditional Random Fields for Prediction of Web User Behavior},
volume = {5476},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/ecoc-crf-2009.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-01307-2_77}
}
@inproceedings{guo2009.1,
author = {Yong Zhen Guo and Kotagiri Ramamohanarao and Laurence A.~F.~Park},
booktitle = {Web Information Systems and Mining},
doi = {10.1007/978-3-642-05250-7_4},
issn = {0302-9743},
keywords = {laurence},
month = {November},
pages = {31-44},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
title = {Web Access Latency Reduction using {CRF}-based Predictive Caching},
volume = {5854},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/crf-caching-2009.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-05250-7_4}
}
@inproceedings{park2009,
abstract = { Spectral co-clustering is a generic method of
computing co- clusters of relational data, such as
sets of documents and their terms. Latent semantic
analysis is a method of document and term smoothing
that can assist in the information retrieval
process. In this article we ex- amine the process
behind spectral clustering for documents and terms,
and compare it to Latent Semantic Analysis. We show
that both spectral co-clustering and LSA follow the
same process, using different normal- isation
schemes and metrics. By combining the properties of
the two co-clustering methods, we obtain an improved
co-clustering method for document-term relational
data that provides an increase in the cluster
quality of 33.0\%.},
author = {Laurence A.~F.~Park and Kotagiri Ramamohanarao and Christopher A.~Leckie and James C. Bezdek},
booktitle = {AI 2009: Advances in Artificial Intelligence},
date-added = {2008-10-24 09:17:10 +1100},
date-modified = {2009-09-10 12:27:10 +0200},
doi = {10.1007/978-3-642-10439-8_31},
keywords = {laurence},
month = {December},
pages = {301-311},
publisher = {Springer Berlin / Heidelberg},
series = {Lecture Notes in Computer Science},
title = {Adapting spectral co-clustering to documents and words using latent semantic analysis},
url = {http://www.cs.mu.oz.au/~lapark/document-cluster.pdf},
volume = {5866},
year = {2009},
bdsk-url-1 = {http://www.cs.mu.oz.au/~lapark/document-cluster.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1007/978-3-642-10439-8_31}
}
@incollection{park2008.9,
address = {Bled, Slovenia},
author = {Laurence A.~F.~Park and Kotagiri Ramamohanarao},
booktitle = {Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD Proceedings, Part II},
date-added = {2008-06-30 15:19:20 +1000},
date-modified = {2009-09-10 12:18:22 +0200},
doi = {10.1007/978-3-642-04174-7_12},
editor = {Wray Buntine and Marko Grobelnik and Dunja Mladeni\`{c} and John Shawe-Taylor},
keywords = {laurence},
month = {September},
pages = {176-188},
publisher = {Springer},
series = {Lecture Notes in Artificial Intelligence},
title = {The sensitivity of Latent Dirichlet Allocation for Information Retrieval},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/lda-ir-2009.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1007/978-3-642-04174-7_12}
}
@incollection{park2008.5,
author = {Justin Zobel and Alistair Moffat and Laurence A.~F.~Park},
booktitle = {ACM SIGIR Forum},
date-added = {2008-06-30 15:08:17 +1000},
date-modified = {2009-06-19 15:37:22 +1000},
keywords = {laurence},
month = {June},
number = {1},
pages = {3-15},
publisher = {ACM Press},
title = {Against Recall: Is it Persistence, Cardinality, Density, Coverage, or Totality?},
url = {http://www.sigir.org/forum/2009J/2009j-sigirforum-zobel.pdf},
volume = {43},
year = {2009},
bdsk-url-1 = {http://www.sigir.org/forum/2009J/2009j-sigirforum-zobel.pdf}
}
@inproceedings{park2008.3,
abstract = {A matrix D, of pairwise dissimilarities between m row objects and n column objects, can be clustered: amongst row objects or column objects; amongst the union of row and column objects; and amongst the union of row and column objects containing at least one object of each type (co-clusters). The coVAT algorithm, which builds images for visual assessment of clustering tendency for these problems, is limited to mn~O(10^4*10^4). We develop a scalable version of coVAT that approximates coVAT images when D is very large. Two examples are given to illustrate and evaluate the new method.},
author = {Laurence A.~F.~Park and James C.~Bezdek and Christopher A.~Leckie},
booktitle = {Proceedings of the Fourth International Conference on Autonomous Robots and Agents},
date-added = {2008-06-30 15:04:54 +1000},
date-modified = {2009-04-22 12:39:24 +1000},
doi = {10.1109/ICARA.2000.4803948},
editor = {G. Sen Gupta and S. C. Mukhopadhyay},
keywords = {laurence},
month = {February},
pages = {251-256},
title = {Visualisation of Clusters in Very Large Rectangular Dissimilarity Data},
url = {http://www.cs.mu.oz.au/~lapark/scoVAT2.pdf},
year = {2009},
bdsk-url-1 = {http://www.cs.mu.oz.au/~lapark/scoVAT2.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1109/ICARA.2000.4803948}
}
@inproceedings{park2007.3,
author = {Laurence A. F. Park and Kotagiri Ramamohanarao},
booktitle = {Proceeding of the 18th ACM conference on Information and knowledge management.},
date-added = {2007-07-24 09:58:03 +0200},
date-modified = {2009-09-10 12:31:42 +0200},
doi = {10.1145/1645953.1646214},
editor = {David Cheung and Il-Yeol Song and Wesley Chu and Xiaohua Hu and Jimmy Lin and Jiexun Li and Zhiyong Peng},
keywords = {laurence},
month = {November},
pages = {1721-1724},
publisher = {The Association for Computing Machinery},
title = {Kernel latent semantic analysis using an information retrieval based kernel},
year = {2009},
url = {http://staff.scem.uws.edu.au/~lapark/publications/klsa-ir-2009.pdf},
bdsk-url-1 = {http://dx.doi.org/10.1145/1645953.1646214}
}
@article{park2007,
abstract = {Probabilistic latent semantic analysis (PLSA) is a
method for computing term and document relationships
from a document set. The probabilistic latent
semantic index (PLSI) has been used to store PLSA
information, but unfortunately the PLSI uses
excessive storage space relative to a simple term
frequency index, which causes lengthy query
times. To overcome the storage and speed problems of
PLSI, we introduce the probabilistic latent semantic
thesaurus (PLST); an efficient and effective method
of storing the PLSA information. We show that
through methods such as document thresholding and
term pruning, we are able to maintain the high
precision results found using PLSA while using a
very small percent (0.15\%) of the storage space of
PLSI.},
author = {Laurence A. F. Park and Kotagiri Ramamohanarao},
bibsource = {DBLP, http://dblp.uni-trier.de},
date-added = {2007-07-24 06:43:57 +0200},
date-modified = {2008-11-20 11:02:37 +1100},
doi = {10.1007/s00778-008-0093-2},
ee = {http://dx.doi.org/10.1007/s00778-008-0093-2},
crossref = {VLDB},
keywords = {probabilistic latent semantic analysis; laurence},
month = {January},
number = {1},
pages = {141-156},
title = {Efficient storage and retrieval of probabilistic latent semantic information for Information Retrieval},
url = {http://www.csse.unimelb.edu.au/~lapark/plsi_plst.pdf},
volume = {18},
year = {2009},
contribution = {65\%},
era2010rank = {A*},
sniprank = {5.78},
citations = {20},
bdsk-url-1 = {http://www.csse.unimelb.edu.au/~lapark/plsi_plst.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1007/s00778-008-0093-2}
}
@article{VLDB,
journal = {The International Journal on Very Large Data Bases},
era = {A*},
core = {A+}
}
@article{park2005,
abstract = { Latent semantic analysis (LSA) is a generalised
vector space method (GVSM) that uses dimension
reduction to generate term correlations for use
during the information retrieval process. We
hypothesised that even though the dimension
reduction establishes correlations between terms,
the reduction is causing a degradation in the
correlation of a term to itself
(self-correlation). In this article, we have proven
that there is a direct relationship to the size of
the LSA dimension reduction and the LSA
self-correlation. We have also shown that by
altering the LSA term self-correlations we gain a
significant increase in precision during the
information retrieval process. },
address = {New York, NY, USA},
author = {Laurence A.~F.~Park and Kotagiri Ramamohanarao},
date-modified = {2008-10-24 11:23:01 +1100},
doi = {10.1145/1462198.1462200},
issn = {1046-8188},
journal = {ACM Transactions on Information Systems},
keywords = {latent semantic analysis; laurence},
number = {2},
pages = {1--35},
publisher = {ACM},
title = {An analysis of latent semantic term self-correlation},
url = {http://www.cs.mu.oz.au/~lapark/lsaLargeDataProblem.pdf},
volume = {27},
year = {2009},
contribution = {75\%},
era2010rank = {A},
sniprank = {5.53},
citations = {17},
bdsk-url-1 = {http://www.cs.mu.oz.au/~lapark/lsaLargeDataProblem.pdf},
bdsk-url-2 = {http://dx.doi.org/10.1145/1462198.1462200}
}
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