Publicacion de Papers y Plagio
[tildes omitidas]
Hace muy poco recibi un correo que anexo lineas mas abajo relatando un caso de plagio en la publicacion de papers. Me gustaria compartir algunas ideas que talvez sean utiles para ustedes.
1) Enviar un mismo paper simultaneamente a diversas conferencias es un pecado. Una vez un alumno comento que sus profesores lo incentivaron a hacerlo pues segun el, para ampliar las probabilidades de aceptacion. Esa no es una practica respetable. Es valido enviar a una conferencia, y en caso de “Rejected” enviarlo nuevamente a otra.
2) Otro pecado es intentar aumentar el numero de publicaciones enviando el mismo paper con titulos diferentes a diversos lugares. Inclusive hacer solamente pequenhas modificaciones no es valido. Un nuevo paper es aquel que propone nuevas contribuciones.
3) Tengan mucho cuidado en la hora de copiar texto de otros lugares, los referees estan cada vez mas atentos a eso. No es recomendable. Aun, si algo muy minimo es copiado, este debe ser referenciado, si no estaran incurriendo en otro pecado.
Aconsejo muchos cuidados en la hora de escribir. Si alguien es descubierto como pecador, adios mundo academico.
—– Original Message —– From: “Eamonn Keogh”
Sent: Wednesday, May 02, 2007 11:49 AM
Subject: [Dbworld] Plagiarism: MULTIMETER: A Universal Clustering Algorithm
Dear Colleagues. Forgive the vagueness of this announcement. There is
a reason for it.
Some individual or individuals wrote a paper with the title
“MULTIMETER: A Universal Clustering Algorithm”, and sent it to (at
least) 5 venues in the last month (3 journals and 2 conferences). This
in itself is a bit of a problem, however this sin is compounded by the
fact that virtually of the text is taken from other papers (see
below).
I have been in touch with the individual or individuals, but they are
less than forthcoming.
If you are reviewing a paper with this title, you might want to check
to see if your version of the paper is also plagiarized. If it is, do
what you want, but I would ask you send me an email which I will treat
with the strictest confidence.
Eamonn
MULTIMETER: A Universal Clustering Algorithm
“Clustering in data mining is a discovery process that groups a set of
data such that the intra-cluster similarity is maximized and the
inter-cluster similarity is minimized. In this paper, we present a
novel clustering algorithm called MULTIMETER that measures the
similarity of two clusters based on a dynamic model. In the clustering
process, two clusters are merged only if the inter-connectivity and
closeness(proximity) between two clusters are high relative to the
internal inter-connectivity of the clusters and closeness of items
within the clusters. The methodology of dynamic modeling of clusters
used in MULTIMETER is applicable to all types of data as long as a
similarity matrix can be constructed….”
CHAMELEON [a]
Clustering in data mining is a discovery process that groups a set of
data such that the intracluster similarity is maximized and the
intercluster similarity is minimized. . this paper, we present a novel
hierarchical clustering algorithm called CHAMELEON that measures the
similarity of two clusters based on a dynamic model. In the clustering
process, two clusters are merged only if the inter-connectivity and
closeness (proximity) between two clusters are high relative to the
internal inter-connectivity of the clusters and closeness of items
within the clusters. The merging process using the dynamic model
presented in this paper facilitates discovery of natural and
homogeneous clusters. The methodology of dynamic modeling of clusters
used in CHAMELEON is applicable to all types of data as long as a
similarity matrix can be constructed. .
(And much more, this is only a sample)
[a] CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling 1999
George Karypis, Eui-Hong Han, Vipin Kumar
