Mining Sequential Data – in Search of Segmental Structures

Segmentation is a data mining technique yielding simplified representations of sequences of ordered points. A sequence is divided into some number of homogeneous blocks, and all points within a segment are described by a single value. The focus in this thesis is on piecewise-constant segments, where the most likely description for each segment and the most likely segmentation into some number of blocks can be computed efficiently. Representing sequences as segmentations is useful in, e.g., storage and indexing tasks in sequence databases, and segmentation can be used as a tool in learning about the structure of a given sequence. The discussion in this thesis begins with basic questions related to segmentation analysis, such as choosing the number of segments, and evaluating the obtained segmentations. Standard model selection techniques are shown to perform well for the sequence segmentation task. Segmentation evaluation is proposed with respect to a known segmentation structure. Applying segmentation on certain features of a sequence is shown to yield segmentations that are significantly close to the known underlying structure. Two extensions to the basic segmentation framework are introduced: unimodal segmentation and basis segmentation. The former is concerned with segmentations where the segment descriptions first increase and then decrease, and the latter with the interplay between different dimensions and segments in the sequence. These problems are formally defined and algorithms for solving them are provided and analyzed. Practical applications for segmentation techniques include time series and data stream analysis, text analysis, and biological sequence analysis. In this thesis segmentation applications are demonstrated in analyzing genomic sequences.

Estimation of local forest attributes, utilizing two-phase sampling and auxiliary data

This thesis examines the feasibility of a forest inventory method based on two-phase sampling in estimating forest attributes at the stand or substand levels for forest management purposes. The method is based on multi-source forest inventory combining auxiliary data consisting of remote sensing imagery or other geographic information and field measurements. Auxiliary data are utilized as first-phase data for covering all inventory units. Various methods were examined for improving the accuracy of the forest estimates. Pre-processing of auxiliary data in the form of correcting the spectral properties of aerial imagery was examined (I), as was the selection of aerial image features for estimating forest attributes (II). Various spatial units were compared for extracting image features in a remote sensing aided forest inventory utilizing very high resolution imagery (III). A number of data sources were combined and different weighting procedures were tested in estimating forest attributes (IV, V). Correction of the spectral properties of aerial images proved to be a straightforward and advantageous method for improving the correlation between the image features and the measured forest attributes. Testing different image features that can be extracted from aerial photographs (and other very high resolution images) showed that the images contain a wealth of relevant information that can be extracted only by utilizing the spatial organization of the image pixel values. Furthermore, careful selection of image features for the inventory task generally gives better results than inputting all extractable features to the estimation procedure. When the spatial units for extracting very high resolution image features were examined, an approach based on image segmentation generally showed advantages compared with a traditional sample plot-based approach. Combining several data sources resulted in more accurate estimates than any of the individual data sources alone. The best combined estimate can be derived by weighting the estimates produced by the individual data sources by the inverse values of their mean square errors. Despite the fact that the plot-level estimation accuracy in two-phase sampling inventory can be improved in many ways, the accuracy of forest estimates based mainly on single-view satellite and aerial imagery is a relatively poor basis for making stand-level management decisions.

Enhanced processing of SPOT multispectral satellite imagery for environmental monitoring and modelling

The Taita Hills in southeastern Kenya form the northernmost part of Africa’s Eastern Arc Mountains, which have been identified by Conservation International as one of the top ten biodiversity hotspots on Earth. As with many areas of the developing world, over recent decades the Taita Hills have experienced significant population growth leading to associated major changes in land use and land cover (LULC), as well as escalating land degradation, particularly soil erosion. Multi-temporal medium resolution multispectral optical satellite data, such as imagery from the SPOT HRV, HRVIR, and HRG sensors, provides a valuable source of information for environmental monitoring and modelling at a landscape level at local and regional scales. However, utilization of multi-temporal SPOT data in quantitative remote sensing studies requires the removal of atmospheric effects and the derivation of surface reflectance factor. Furthermore, for areas of rugged terrain, such as the Taita Hills, topographic correction is necessary to derive comparable reflectance throughout a SPOT scene. Reliable monitoring of LULC change over time and modelling of land degradation and human population distribution and abundance are of crucial importance to sustainable development, natural resource management, biodiversity conservation, and understanding and mitigating climate change and its impacts. The main purpose of this thesis was to develop and validate enhanced processing of SPOT satellite imagery for use in environmental monitoring and modelling at a landscape level, in regions of the developing world with limited ancillary data availability. The Taita Hills formed the application study site, whilst the Helsinki metropolitan region was used as a control site for validation and assessment of the applied atmospheric correction techniques, where multiangular reflectance field measurements were taken and where horizontal visibility meteorological data concurrent with image acquisition were available. The proposed historical empirical line method (HELM) for absolute atmospheric correction was found to be the only applied technique that could derive surface reflectance factor within an RMSE of < 0.02 ps in the SPOT visible and near-infrared bands; an accuracy level identified as a benchmark for successful atmospheric correction. A multi-scale segmentation/object relationship modelling (MSS/ORM) approach was applied to map LULC in the Taita Hills from the multi-temporal SPOT imagery. This object-based procedure was shown to derive significant improvements over a uni-scale maximum-likelihood technique. The derived LULC data was used in combination with low cost GIS geospatial layers describing elevation, rainfall and soil type, to model degradation in the Taita Hills in the form of potential soil loss, utilizing the simple universal soil loss equation (USLE). Furthermore, human population distribution and abundance were modelled with satisfactory results using only SPOT and GIS derived data and non-Gaussian predictive modelling techniques. The SPOT derived LULC data was found to be unnecessary as a predictor because the first and second order image texture measurements had greater power to explain variation in dwelling unit occurrence and abundance. The ability of the procedures to be implemented locally in the developing world using low-cost or freely available data and software was considered. The techniques discussed in this thesis are considered equally applicable to other medium- and high-resolution optical satellite imagery, as well the utilized SPOT data.

Problems and Algorithms for Sequence Segmentations

The analysis of sequential data is required in many diverse areas such as telecommunications, stock market analysis, and bioinformatics. A basic problem related to the analysis of sequential data is the sequence segmentation problem. A sequence segmentation is a partition of the sequence into a number of non-overlapping segments that cover all data points, such that each segment is as homogeneous as possible. This problem can be solved optimally using a standard dynamic programming algorithm. In the first part of the thesis, we present a new approximation algorithm for the sequence segmentation problem. This algorithm has smaller running time than the optimal dynamic programming algorithm, while it has bounded approximation ratio. The basic idea is to divide the input sequence into subsequences, solve the problem optimally in each subsequence, and then appropriately combine the solutions to the subproblems into one final solution. In the second part of the thesis, we study alternative segmentation models that are devised to better fit the data. More specifically, we focus on clustered segmentations and segmentations with rearrangements. While in the standard segmentation of a multidimensional sequence all dimensions share the same segment boundaries, in a clustered segmentation the multidimensional sequence is segmented in such a way that dimensions are allowed to form clusters. Each cluster of dimensions is then segmented separately. We formally define the problem of clustered segmentations and we experimentally show that segmenting sequences using this segmentation model, leads to solutions with smaller error for the same model cost. Segmentation with rearrangements is a novel variation to the segmentation problem: in addition to partitioning the sequence we also seek to apply a limited amount of reordering, so that the overall representation error is minimized. We formulate the problem of segmentation with rearrangements and we show that it is an NP-hard problem to solve or even to approximate. We devise effective algorithms for the proposed problem, combining ideas from dynamic programming and outlier detection algorithms in sequences. In the final part of the thesis, we discuss the problem of aggregating results of segmentation algorithms on the same set of data points. In this case, we are interested in producing a partitioning of the data that agrees as much as possible with the input partitions. We show that this problem can be solved optimally in polynomial time using dynamic programming. Furthermore, we show that not all data points are candidates for segment boundaries in the optimal solution.

Dental Service Utilization, Dental Health Production and Equity in Dental Care: the Finnish Experience

This thesis is grounded on four articles. Article I generally examines the factors affecting dental service utilization. Article II studies the factors associated with sector-specific utilization among young adults entitled to age-based subsidized dental care. Article III explores the determinants of dental ill-health as measured by the occurrence of caries and the relationship between dental ill-health and dental care use. Article IV measures and explains income-related inequality in utilization. Data employed were from the 1996 Finnish Health Care Survey (I, II, IV) and the 1997 follow-up study included in the longitudinal study of the Northern Finland 1966 Birth Cohort (III). Utilization is considered as a multi-stage decision-making process and measured as the number of visits to the dentist. Modified count data models and concentration and horizontal equity indices were applied. Dentist s recall appeared very efficient at stimulating individuals to seek care. Dental pain, recall, and the low number of missing teeth positively affected utilization. Public subvention for dental care did not seem to statistically increase utilization. Among young adults, a perception of insufficient public service availability and recall were positively associated with the choice of a private dentist, whereas income and dentist density were positively associated with the number of visits to private dentists. Among cohort females, factors increasing caries were body mass index and intake of alcohol, sugar, and soft drinks and those reducing caries were birth weight and adolescent school achievement. Among cohort males, caries was positively related to the metropolitan residence and negatively related to healthy diet and education. Smoking increased caries, whereas regular teeth brushing, regular dental attendance and dental care use decreased caries. We found equity in young adults utilization but pro-rich inequity in the total number of visits to all dentists and in the probability of visiting a dentist for the whole sample. We observed inequity in the total number of visits to the dentist and in the probability of visiting a dentist, being pro-poor for public care but pro-rich for private care. The findings suggest that to enhance equal access to and use of dental care across population and income groups, attention should focus on supply factors and incentives to encourage people to contact dentists more often. Lowering co-payments and service fees and improving public availability would likely increase service use in both sectors. To attain favorable oral health, appropriate policies aimed at improving dental health education and reducing the detrimental effects of common risk factors on dental health should be strengthened. Providing equal access with respect to need for all people ought to take account of the segmentation of the service system, with its two parallel delivery systems and different supplier incentives to patients and dentists.

Does relationship marketing improve customer relationship satisfaction and loyalty?

Purpose - This study investigates the relationship marketing (RM) strategy of a retail bank and examines whether - after its implementation - customer relationships were strengthened through perceived improvements in the banking relationship and consequent loyalty towards the bank. Design/methodology/approach - A survey was conducted on two profitability segments, of which the more profitable segment had been directly exposed to a customer oriented RM strategy, whereas the less profitable segment had been subjected to more sales oriented marketing communications. Findings - No significant differences were found between the segments on customers’ evaluations of the service relationship or their loyalty toward the bank. Furthermore regression analysis revealed that relationship satisfaction was less important as a determinant of loyalty in the more profitable segment. Research limitations/implications - This study was conducted as a case study of one specific branch of a bank group in Finland, which limits the external validity of its results. It was not possible to ascertain if, or to what extent, customers of the more profitable segment had received the intended RM treatment. Other limitations are also discussed. Practical implications - Customer orientation is desirable within retail banking and more studies are needed on the differential drivers of loyalty across customer profitability segments. By identifying the aspects of a banking relationship that are more highly valued among more profitable customers than among less profitable customers, bank managers would be able to more effectively devise appropriate strategies for different segments. Originality/value - The study contributes to the RM literature and marketing of financial services by providing empirical evidence of the effects of RM activities on customer relationship perceptions in different profitability segments.

Panel Cointegration of Chinese A and B Shares

This paper uses panel unit root and cointegration methods to test the stationarity of the premium on domestic investors’ A shares over foreign investors’ B shares and cointegration between the A and B share prices on the Chinese stock exchanges. We find that the A share price premium is nonstationary until 2001, when the A and B share markets were partially merged, and that the A and B share prices are cointegrated in the panel.Cointegration is more likely to be found for firms in the service sector and for firms that issued B shares recently.

From One Segment to a Segment of One - The Evolution of Market Segmentation Theory

The purpose of this study was to deepen the understanding of market segmentation theory by studying the evolution of the concept and by identifying the antecedents and consequences of the theory. The research method was influenced by content analysis and meta-analysis. The evolution of market segmentation theory was studied as a reflection of evolution of marketing theory. According to this study, the theory of market segmentation has its roots in microeconomics and it has been influenced by different disciplines, such as motivation research and buyer behaviour theory. Furthermore, this study suggests that the evolution of market segmentation theory can be divided into four major eras: the era of foundations, development and blossoming, stillness and stagnation, and the era of re-emergence. Market segmentation theory emerged in the mid-1950’s and flourished during the period between mid-1950’s and the late 1970’s. During the 1980’s the theory lost its interest in the scientific community and no significant contributions were made. Now, towards the dawn of the new millennium, new approaches have emerged and market segmentation has gained new attention.