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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Anna Muratova
Classification Techniques for Pattern Mining in Demographic Sequences
Data Science
(Master’s programme)
9
2017
Nowadays there is a large amount of demographic data, which should be analyzed and interpreted. From accumulated demographic data, more useful information can be extracted by applying modern methods of mining data.

Data for the work was obtained from the scientific laboratory of socio-demographic policy at HSE and it contains results of a survey of 6626 people including 3314 men and 3312 women. In the database for each person the date of significant events in their lives are indicated, such as: partner, marriage, break up, divorce, education, work, separation from parents and birth of a child. Also there are features of people: type of education (general, higher, professional), locality (city, town, country), religion, frequency of church attendance, generation (Soviet 1930-1969 and modern 1970-1986) and gender.

In the work, formulas for the calculation of sequence similarity measures without discontinuity were derived and proved. Then they were used as kernels in the SVM method.

The aim of the work was to compare the methods of classification of demographic data by customizing the SVM kernels using various similarity measures. Neural network algorithms are also compared.

Together with demographers, a universal algorithm for data cleaning was developed and implemented, as the data contains a large number of various errors and misprints.

To complete the work, programs in Python were written, with the help of which the original demographic data was processed.

The best classification results are obtained using a special kernel function in SVM for sequence processing, as well as using a recurrent neural network.

The novelty of the work is the use of special kernel variants in the SVM method. In addition, the results were improved with the help of neural network algorithms.

Keywords: data mining, demographics, support vector machines, neural networks, kernels, prefix, substrings, similarity.

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