%0 Journal Article %1 keyhere %A Ramakrishnan, Raghu %A Chen, Bee-Chung %D 2007 %J Data Mining and Knowledge Discovery %K datamining olap survey %N 1 %P 29--54 %T Exploratory mining in cube space %U http://dx.doi.org/10.1007/s10618-007-0063-0 %V 15 %X Abstract  Data Mining has evolved as a new discipline at the intersection of several existing areas, including Database Systems, Machine Learning, Optimization, and Statistics. An important question is whether the field has matured to the point where it has originatedsubstantial new problems and techniques that distinguish it from its parent disciplines. In this paper, we discuss a classof new problems and techniques that show great promise for exploratory mining, while synthesizing and generalizing ideas fromthe parent disciplines. While the class of problems we discuss is broad, there is a common underlying objective—to look beyonda single data-mining step (e.g., data summarization or model construction) and address the combined process of data selectionand transformation, parameter and algorithm selection, and model construction. The fundamental difficulty lies in the largespace of alternative choices at each step, and good solutions must provide a natural framework for managing this complexity.We regard this as a grand challenge for Data Mining, and see the ideas discussed here as promising initial steps towards arigorous exploratory framework that supports the entire process.

@article{keyhere, abstract = {Abstract  Data Mining has evolved as a new discipline at the intersection of several existing areas, including Database Systems, Machine Learning, Optimization, and Statistics. An important question is whether the field has matured to the point where it has originatedsubstantial new problems and techniques that distinguish it from its parent disciplines. In this paper, we discuss a classof new problems and techniques that show great promise for exploratory mining, while synthesizing and generalizing ideas fromthe parent disciplines. While the class of problems we discuss is broad, there is a common underlying objective—to look beyonda single data-mining step (e.g., data summarization or model construction) and address the combined process of data selectionand transformation, parameter and algorithm selection, and model construction. The fundamental difficulty lies in the largespace of alternative choices at each step, and good solutions must provide a natural framework for managing this complexity.We regard this as a grand challenge for Data Mining, and see the ideas discussed here as promising initial steps towards arigorous exploratory framework that supports the entire process.}, added-at = {2007-12-06T05:20:36.000+0100}, author = {Ramakrishnan, Raghu and Chen, Bee-Chung}, biburl = {https://www.bibsonomy.org/bibtex/235f204165abf8a601efab79bdc7226f1/jhammerb}, description = {SpringerLink - Journal Article}, interhash = {7933eb8dbadea71e795dce7fd58d567c}, intrahash = {35f204165abf8a601efab79bdc7226f1}, journal = {Data Mining and Knowledge Discovery}, keywords = {datamining olap survey}, month = {#aug#}, number = 1, pages = {29--54}, timestamp = {2007-12-06T05:20:36.000+0100}, title = {Exploratory mining in cube space}, url = {http://dx.doi.org/10.1007/s10618-007-0063-0}, volume = 15, year = 2007 }