Datasets

Datasets#

Wireless Indoor Localization Dataset#

The wireless data frame has 2000 rows and 8 columns. The first 7 variables report the measurements of the Wi-Fi signal strength received from 7 Wi-Fi routers in an office location in Pittsburgh (USA). The last column indicates the class labels.

Format#

A data frame containing the following columns:

  • V1: signal strength from router 1.

  • V2: signal strength from router 2.

  • V3: signal strength from router 3.

  • V4: signal strength from router 4.

  • V5: signal strength from router 5.

  • V6: signal strength from router 6.

  • V7: signal strength from router 7.

  • V8: group memberships, from 1 to 4.

Details#

The Wi-Fi signal strength is measured in dBm, decibel milliwatts, which is expressed as a negative value ranging from -100 to 0. The labels correspond to ‘conference room’, ‘kitchen’, ‘indoor sports room’, and ‘other’. In total, we have 4 groups with 500 observations each.

Source#

Bhatt, R. (2017). Wireless Indoor Localization. UCI Machine Learning Repository. https://doi.org/10.24432/C51880.

References#

Rohra, J.G., Perumal, B., Narayanan, S.J., Thakur, P., Bhatt, R.B. (2017). User Localization in an Indoor Environment Using Fuzzy Hybrid of Particle Swarm Optimization & Gravitational Search Algorithm with Neural Networks. In: Deep, K., et al. Proceedings of Sixth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 546. Springer, Singapore. https://doi.org/10.1007/978-981-10-3322-3_27

Breast Cancer Wisconsin (Diagnostic)#

The wisconsin breast cancer dataset data frame has 569 rows and 31 columns. The first 30 variables report the features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. The last column indicates the class labels (Benign = 0 or Malignant = 1).

Source#

Wolberg,William, Mangasarian,Olvi, Street,Nick, and Street,W.. (1995). Breast Cancer Wisconsin (Diagnostic). UCI Machine Learning Repository. https://doi.org/10.24432/C5DW2B.

References#

Street, W. N., Wolberg, W. H., & Mangasarian, O. L. (1993, July). Nuclear feature extraction for breast tumor diagnosis. In Biomedical image processing and biomedical visualization (Vol. 1905, pp. 861-870). SPIE.

Wine Dataset#

The wine data frame has 178 rows and 14 columns. The first 13 variables report 13 constituents found in each of the three types of wines. The last column indicates the class labels (1,2 or 3).

Format#

A data frame containing the following columns:

  • Alcohol

  • Malic acid

  • Ash

  • Alcalinity of ash

  • Magnesium

  • Total phenols

  • Flavanoids

  • Nonflavanoid phenols

  • Proanthocyanins

  • Color intensity

  • Hue

  • OD280/OD315 of diluted wines

  • Proline

  • Class

Details#

These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars. The analysis determined the quantities of 13 constituents found in each of the three types of wines.

Source#

Aeberhard, S. & Forina, M. (1992). Wine [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5PC7J.

References#

Aeberhard, S., Coomans, D., & De Vel, O. (1994). Comparative analysis of statistical pattern recognition methods in high dimensional settings. Pattern Recognition, 27(8), 1065-1077.