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Association rule mining through adaptive parameter control in particle swarm optimization

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Abstract

Association rule mining is a data mining task on a great deal of academic research has been done and many algorithms are proposed. Association rule mining is treated as a twofold process by most of the methods. It increases the complexity of the system and takes up more time and space. Evolutionary Computation (EC) are fast growing search based optimization method for association rule mining. Among ECs particle swarm optimization (PSO) is more suited for mining association rules. The bottleneck of PSO is setting the precise values for their control parameters. Setting values to the control parameter is done either through parameter tuning or parameter control. This paper proposes an adaptive methodology for the control parameters in PSO namely, acceleration coefficients and inertia weight based on estimation of evolution state and fitness value respectively. Both of the proposed adaptive methods when tested on five datasets from University of California Irvine (UCI) repository proved to generate association rules with better accuracy and rule measures compared to simple PSO.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Pondicherry Engineering College, Puducherry, India

    K. Indira

  2. Department of Information Technology, Pondicherry Engineering College, Puducherry, India

    S. Kanmani

Authors
  1. K. Indira
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  2. S. Kanmani
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Corresponding author

Correspondence to K. Indira.

Appendices

Appendix 1

1.1 UCI Datasets

The datasets that have been used in this research work have been taken from the UCI machine learning repository. The UCI Machine Learning Repository is a collection of databases, domain theories, and data generators that are used by the machine learning community for the empirical analysis of machine learning algorithms. The archive was created as an ftp archive in 1987 by David Aha and fellow graduate students at UC Irvine. The details of the datasets used in this study from http://archive.ics.uci.edu/ml/about.html are presented below.

Lenses dataset

  • Number of Instances: 24

  • Number of Attributes: 4 (all nominal)

  • Attribute Information: 3 Classes

  1. 1:

    the patient should be fitted with hard contact lenses,

  2. 2:

    the patient should be fitted with soft contact lenses,

  3. 3:

    the patient should not be fitted with contact lenses.

    • age of the patient: (1) young, (2) pre-presbyopic, (3) presbyopic

    • spectacle prescription: (1) myope, (2) hypermetrope

    • astigmatic: (1) no, (2) yes

    • tear production rate: (1) reduced, (2) normal

  • Number of Missing Attribute Values: 0

Haberman’s survival dataset

  • Number of Instances: 306

  • Number of Attributes: 4 (including the class attribute)

  • Attribute Information:

    1. 1.

      Age of patient at time of operation (numerical)

    2. 2.

      Patient’s year of operation (year—1900, numerical)

    3. 3.

      Number of positive axillary nodes detected (numerical)

    4. 4.

      Survival status (class attribute)

      • 1 = the patient survived 5 years or longer

      • 2 = the patient died within 5 year

  • Missing Attribute Values: None

Car evaluation dataset

  • Number of Instances: 1728 (instances completely cover the attribute space)

  • Number of Attributes: 6

  • Attribute Values:

    buying:

    v-high, high, med, low

    maint:

    v-high, high, med, low

    doors:

    2, 3, 4, 5-more

    persons:

    2, 4, more

    lug_boot:

    small, med, big

    safety:

    low, med, high

  • Missing Attribute Values: none

Post operative patient dataset

  • Number of Instances: 90

  • Number of Attributes: 9 including the decision (class attribute)

  • Attribute Information:

    1. 1.

      L-CORE (patient’s internal temperature in C):

    high (\(>\) 37), mid (\(>\)= 36 and \(<\)= 37), low (\(<\) 36)

    1. 2.

      L-SURF (patient’s surface temperature in C):

    high (\(>\) 36.5), mid (\(>\)= 36.5 and \(<\)= 35), low (\(<\) 35)

    1. 3.

      L-O2 (oxygen saturation in %):

    excellent (\(>\)= 98), good (\(>\)= 90 and \(<\) 98), fair (\(>\)= 80 and \(<\) 90), poor (\(<\) 80)

    1. 4.

      L-BP (last measurement of blood pressure):

    high (\(>\) 130/90), mid (\(<\)= 130/90 and \(>\)= 90/70), low (\(<\) 90/70)

    1. 5.

      SURF-STBL (stability of patient’s surface temperature):

    stable, mod-stable, unstable

    1. 6.

      CORE-STBL (stability of patient’s core temperature)

    stable, mod-stable, unstable

    1. 7.

      BP-STBL (stability of patient’s blood pressure)

    stable, mod-stable, unstable

    1. 8.

      COMFORT (patient’s perceived comfort at discharge, measured as

    an integer between 0 and 20)

    1. 9.

      decision ADM-DECS (discharge decision):

      • I (patient sent to Intensive Care Unit),

      • S (patient prepared to go home),

      • A (patient sent to general hospital floor)

  • Missing Attribute Values: Attribute 8 has 3 missing values

Zoo dataset

  • Number of Instances: 101

  • Number of Attributes: 18 (animal name, 15 Boolean attributes, 2 numerics)

  • Attribute Information: (name of attribute and type of value domain)

    1. animal name:

    Unique for each instance

    2. hair:

    Boolean

    3. feathers:

    Boolean

    4. eggs:

    Boolean

    5. milk:

    Boolean

    6. airborne:

    Boolean

    7. aquatic:

    Boolean

    8. predator:

    Boolean

    9. toothed:

    Boolean

    10. backbone:

    Boolean

    11. breathes:

    Boolean

    12. venomous:

    Boolean

    13. fins:

    Boolean

    14. legs:

    Numeric (set of values: {0,2,4,5,6,8})

    15. tail:

    Boolean

    16. domestic:

    Boolean

    17. catsize:

    Boolean

    18. type:

    Numeric (integer values in range [1,7])

  • Missing Attribute Values: None

Iris dataset

  • Number of Instances: 150 (50 in each of three classes)

  • Number of Attributes: 4 numeric, predictive attributes and the class

  • Attribute Information: Sepal length, Sepal width, Petal length and Petal width in cm

  • Class Types: Iris Setosa, Iris Versicolour and Iris Virginica.

  • Missing Attribute Values: None

Nursery dataset

  • Number of Instances: 12960 (instances completely cover the attribute space)

  • Number of Attributes: 8

  • Attribute Values:

    parents:

    usual, pretentious, great_pret

    has_nurs:

    proper, less_proper, improper, critical, very_crit

    form:

    complete, completed, incomplete, foster

    children:

    1, 2, 3, more

    housing:

    convenient, less_conv, critical

    finance:

    convenient, inconv

    social:

    non-prob, slightly_prob, problematic

    health:

    recommended, priority, not_recom

  • Missing Attribute Values: none

Tic Tac Toe dataset

  • Number of Instances: 958 (legal tic-tac-toe endgame boards)

  • Number of Attributes: 9, each corresponding to one tic-tac-toe square

  • Attribute Information: (x = player x has taken, o = player o has taken, b = blank)

    1. 1.

      top-left-square: {x,o,b}

    2. 2.

      top-middle-square: {x,o,b}

    3. 3.

      top-right-square: {x,o,b}

    4. 4.

      middle-left-square: {x,o,b}

    5. 5.

      middle-middle-square: {x,o,b}

    6. 6.

      middle-right-square: {x,o,b}

    7. 7.

      bottom-left-square: {x,o,b}

    8. 8.

      bottom-middle-square: {x,o,b}

    9. 9.

      bottom-right-square: {x,o,b}

    10. 10.

      Class: {positive,negative}

  • Missing Attribute Values: None

Wisconsin Breast Cancer dataset

  • Number of Instances: 699

  • Number of Attributes: 10 plus the class attribute

  • Attribute Information: Sample code number, Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli, Mitoses and Class (2 for benign, 4 for malignant).

  • Missing attribute values: 16

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Indira, K., Kanmani, S. Association rule mining through adaptive parameter control in particle swarm optimization. Comput Stat 30, 251–277 (2015). https://doi.org/10.1007/s00180-014-0533-y

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