### Abstract

The analysis of binary (0 or 1) data requires an analysis method whose objects are realizations. Yamashita and Suzuki (to appear) proposed principal points for binary distributions based on the concept of principal points, defined by Flury (1990). Ideally, when we search for the binary principal points, all combinations of the k-principal points should be considered; however, this problem cannot be solved in a straightforward manner because the number of combinations increases exponentially when the number of the variables increases. In this paper, we propose three heuristic methods for approximating principal points for binary distributions. The results indicate that our method enables us to find approximated principal points and summarize a binary distribution using the points.

Original language | English |
---|---|

Pages (from-to) | 131-141 |

Number of pages | 11 |

Journal | Journal of Japan Industrial Management Association |

Volume | 65 |

Issue number | 2 |

Publication status | Published - 2014 |

Externally published | Yes |

### Fingerprint

### Keywords

- Binary distributions
- Data analysis
- Heuristic approximation method
- K-means algorithm
- Principal points

### ASJC Scopus subject areas

- Industrial and Manufacturing Engineering
- Applied Mathematics
- Management Science and Operations Research
- Strategy and Management

### Cite this

*Journal of Japan Industrial Management Association*,

*65*(2), 131-141.

**Heuristic approximation methods for principal points for binary distributions.** / Yamashita, Haruka; Suzuki, Hideo.

Research output: Contribution to journal › Article

*Journal of Japan Industrial Management Association*, vol. 65, no. 2, pp. 131-141.

}

TY - JOUR

T1 - Heuristic approximation methods for principal points for binary distributions

AU - Yamashita, Haruka

AU - Suzuki, Hideo

PY - 2014

Y1 - 2014

N2 - The analysis of binary (0 or 1) data requires an analysis method whose objects are realizations. Yamashita and Suzuki (to appear) proposed principal points for binary distributions based on the concept of principal points, defined by Flury (1990). Ideally, when we search for the binary principal points, all combinations of the k-principal points should be considered; however, this problem cannot be solved in a straightforward manner because the number of combinations increases exponentially when the number of the variables increases. In this paper, we propose three heuristic methods for approximating principal points for binary distributions. The results indicate that our method enables us to find approximated principal points and summarize a binary distribution using the points.

AB - The analysis of binary (0 or 1) data requires an analysis method whose objects are realizations. Yamashita and Suzuki (to appear) proposed principal points for binary distributions based on the concept of principal points, defined by Flury (1990). Ideally, when we search for the binary principal points, all combinations of the k-principal points should be considered; however, this problem cannot be solved in a straightforward manner because the number of combinations increases exponentially when the number of the variables increases. In this paper, we propose three heuristic methods for approximating principal points for binary distributions. The results indicate that our method enables us to find approximated principal points and summarize a binary distribution using the points.

KW - Binary distributions

KW - Data analysis

KW - Heuristic approximation method

KW - K-means algorithm

KW - Principal points

UR - http://www.scopus.com/inward/record.url?scp=84923228480&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84923228480&partnerID=8YFLogxK

M3 - Article

VL - 65

SP - 131

EP - 141

JO - Journal of Japan Industrial Management Association

JF - Journal of Japan Industrial Management Association

SN - 0386-4812

IS - 2

ER -