I am involved in research in bioinformatics, classification and data mining. I am the leader of the classification and data mining research group in the Statistics section.
My full publication info is here
Presently, I am particularly interested in a number of things:
enables cell biologist to obtain high resolution 3D images of the
cell nucleus. We are especially interested in the spatial
configuration of sub-nuclear bodies, and the relationship of
configuration to function. This raises new challenges for imaging
and analysis. Some recent work
- McManus, K.J., Stephens, D.A., Adams, N.M., Islam, S. A.,
Freemont, P.S. and Hendzel, M. J., 'The transcriptional regulator CBP has defined spatial associations within interphase nuclei' PLOS Comput. Biol., 2(10), (2006), 1271-1283.
- Shiels, C., Adams, N.M., Stephens, D.A., Islam, S., and
Freemont, P.S., 'Quantitative analysis of cell nuclear organisation' PLOS Comput. Biol., 3(7), (2007), 1161-1168.
- Russell, R.A., Adams, N.M., Stephens, D.A., Batty, E.,
Jensen, K. and Freemont, P.S., 'The stable count thresholding (SCT)
algorithm for segmentation of fluorescence microscopy images',
Biophys. J, 96(8),(2009), 3379-3389.
- Adams, N.M and Freemont, P.S. 'Advances in Nuclear Architecture',
Springer (2010), forthcoming.
This is on-going collaboration
Freemont and Dave Stephens.
Advances in data acquisition technology have enabled the continuous
collection of data in a variety of contexts, notably sensor
networks. Such contexts require data analysis algorithms that operate
with a single view of each data item, and the have the capacity to adapt to
changes in the underlying stochastic mechanism. Some recent work
We have recently been awarded a grant from the BBSRC to continue our
work on the study of nuclear architecture. The project will start in
October. More details to follow.
- Pavlidis, N.G., Tasoulis, D.K., Adams, N.M and Hand, D.J., 'lambda-perceptron: an adaptive classifier for data streams', Pattern Recog., 44(1) (2011), 78-96.
- Tasoulis, D.K., Adams, N.M., and Hand, D.J., 'Selective fusion of out-of-sequence measurements', Information Fusion, 11(2), (2010), 183-191.
- Anagnostopoulos, C., Tasoulis, D.K., Adams, N.M. and Hand,
D.J. 'Temporally adaptive estimation of logistic classifiers on data streams'. Adv. Data An. Classif., 3(3) (2009), 243-261.
This work is partially supported by the ALADDIN project.
Classification problems are ubiquitous, and are a good example of the
effectiveness of Statistics in the real world. I am especially
interested in complicated contexts, like credit card fraud detection. Outputs
from the EPSRC ThinkCrime project include
- Hand, D.J., Whitrow, C, Adams, N.M., Juszczak, P. and Weston,
'Performance criteria for plastic card fraud detection tools' J. Oper. Res. Soc., 58, (2008), 956-962.
- Juszczak, P., Adams, N.M., Hand, D.J., Whitrow, C. and Weston, D.J., 'Off-the-peg and bespoke classifiers for fraud detection' Comput. Stat. Data An., 52, (2008), 4521-4532.
- Weston, D.J., Hand, D.J., Adams, N.M., Whitrow, C., and
'Plastic card fraud detection using peer group analysis' Adv. Data An. Classif., 2(1), (2008), 45-62.
- Whitrow, C., Hand, D.J., Juszczak, P., Weston, D.J., and
Adams, N.M., 'Transaction aggregation as a strategy for credit card
fraud detection', Data Min. Knowl. Disc., 18(1), (2009), 30-55.
- Tasoulis, D.K., Adams, N.M., Weston, D.J. and Hand, D.J., 'Mining information from plastic card transaction streams', in Compstat 2008, Proceedings in Computational Statistics: 18th Symposium, P. Brito (ed), 2008, 315-322.
Data mining involves various types of analysis on large data
sets. We have been involved in defining the sub-area of "pattern
detection and discovery", concerned with finding small local
structures in large data sets. Some work in this area includes
- Hand, D.J., Adams, N.M., and Bolton, R.J. (eds.), 'Pattern
Detection and Discovery' Proceedings of the ESF Exploratory
Workshop, Lecture Notes in Artificial Intelligence, 2477, (Springer:
- Hand, D.J., Adams, N.M., and Heard, N.A. (invited) 'Pattern discovery tools for detecting cheating in student coursework' in 'Local Pattern Detection' Lecture notes in Computer Science 3539, Morik, K. Boulicault, J.-F. and Siebes, A. ed(Springer) 2005, 39-50.
- Cohen, P.R., Adams, N.M., and Heeringa, B. 'Voting Experts: an algorithm for segmenting sequences' Intelligent Data Analysis, 11(6), (2007), 607-625.
- Ross,G, Adams, N.M. Tasoulis, D.K. and Hand, D.J., 'Streaming annotation and prediction for regime switching data streams'. In Dongwan Shin (ed). Proceedings of the 24th Annual ACM Symposium on Applied Computing, Vol III, (2009), 1501-1505
And a recent review-type article on data mining:
- Adams, N.M. (invited), 'Perspectives on Data Mining', Int. J Market Res, 52(1) (2010), 183-191.