The DOI matches its Crossref record.
Introduction to Data Mining
Abstract
In this chapter, we’ll explore the incredibly powerful tools included with SSAS for use in data mining solutions. You can begin by thinking of data mining as a terrific “value add” to your BI solution. Although SSAS 2000 included two data mining algorithms, very few of my clients actually implemented them because deep knowledge about data mining algorithms had not been a typical attribute of SQL Server professionals, and that level of knowledge was necessary to implement data mining using SSAS 2000. As with SSAS OLAP cubes in this 2005 release, Microsoft has made a concerted effort to improve the usability of data mining in SSAS 2005, in part by including a large number of easy-to-use wizards. These wizards contain consistently and remarkably well-documented dialog boxes. However, data mining is a complex topic, and it’s important to understand that this chapter is an introduction to this topic.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
Introduction to Recommender Systems Handbook
Ricci, F., Rokach, L., & Shapira, B. (2011) · Recommender Systems Handbook · doi:10.1007/978-0-387-85820-3_1 · Cited by 2,378
Performance evaluation of classification algorithms by k-fold and leave-one-out cross validation
Wong, T.-T. (2015) · Pattern Recognition · doi:10.1016/j.patcog.2015.03.009 · Cited by 1,562
Shared-nearest-neighbor-based clustering by fast search and find of density peaks
Liu, R., Wang, H., & Yu, X. (2018) · Information Sciences · doi:10.1016/j.ins.2018.03.031 · Cited by 429
Heatwave classification over Europe and the Mediterranean region
Stefanon, M., D’Andrea, F., & Drobinski, P. (2012) · Environmental Research Letters · doi:10.1088/1748-9326/7/1/014023 · Cited by 347
Metadata
- Venue
- Foundations of SQL Server 2005 Business Intelligence
- Publisher
- Apress
- Published
- 2007-09-08
- Pages
- 243-276
- OpenAlex
- W4205774955
- Cited by
- 941
- References
- 0
- FWCI
- 177.42
- Type
- book chapter
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Introduction to Data Mining. https://app.cochat.ai/papers/10.1007/978-1-4302-0248-6_11
- The paper
- Introduction to Data Mining. (2007). Foundations of SQL Server 2005 Business Intelligence. https://doi.org/10.1007/978-1-4302-0248-6_11
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.