Thesis (M. Agricultural Management (Animal Production)) -- University of Limpopo, 2022 / Modern analytical techniques such as data mining algorithms are used to create a model that accurately estimates continuous dependent variable from independent variables of a given set of data. The present study used different data mining algorithms to assess the association between body weight (BW) and morphological characteristics such as body length (BL), heart girth (HG), withers height (WH), rump height (RH), and rump length (RL) of South African non-descript indigenous goats. The research was carried out in the Lepelle-Nkumbi Local Municipality, Capricorn District in the Limpopo province of South Africa. The study used 700 non-descript indigenous goats which include 283 bucks and 417 does with age ranged from one to five years old. The morphological characteristics were taken with a tailor measuring tape and a wood ruler calibrated in centimetres (cm), while the BW was taken with a balanced animal scale calibrated in kilograms (kg). Before the goats were allowed to go for grazing, the following body measurements (BW, BL, HG, WH, RH and RL) was taken once in the morning. Data was analyzed using descriptive statistics, Pearson correlation, various data mining algorithms (Chi-square automatic interaction detector, Classification, and regression tree), analysis of variance and goodness of fit equations (Coefficient of determination (R2), adjusted coefficient of determination (Ajd.R2), root mean square error (RMSE), relative approximate error (RAE), standard deviation ratio (SD. ratio) and coefficient of variance (CV)). The result showed that, BW and HG had higher mean values in does than bucks, BL and WH had higher mean values in bucks than does, and RH and RL had equal mean values in bucks and does, according to descriptive statistics. Furthermore, our findings showed that the BW of does had positive significant correlation (P < 0.01) with BL (r = 0.65), and positive significant correlation (P < 0.05) with HG (r = 0.28), but non-significant correlation (P > 0.05) with WH (r = 0.21), RH (r = 0.23) and RL (r = 0.23). However, the result for bucks indicated that BW had positive significant correlation (P < 0.01) with BL (r = 0.65) but non-significant correlation with HG (r = 0.22), WH (r = 0.07), RH (r = 0.14) and RL (r = 0.12). The chi-square automatic interaction detector and classification and regression tree results indicated that BL in bucks and does had statistical significance (P < 0.01) on BW followed by age, HG, and villages where the animals were raised. Goodness of fit results indicated there was high R2 = 0.58, Adj. R2 = 0.58, and low SD. Ratio = 0.65, RAE = 0.02, RMSE = 5.53) and CV = 14.49 in CHAID model and low R2 = 0.51, Adj. R2 = 0.46 and high SD. Ratio = 0.70, RAE = 0.20, RMSE = 5.95 and CV = 15.49 in CART model. Analysis of variance results indicated that age had significant difference (P < 0.01) on BW and some morphological traits including BL, HG, WH and RH. Sex only revealed significant difference (P < 0.01) in RL. It was concluded that BL alone in both sexes can be used as a selection criterion when determining body weight of goats. Both CHAID and CART suggest that BL alone can be used as a predictor of body weight in goats. Goodness of fit calculations suggest that CHAID is the best model due to its high R2, Adj. R2 and low RAE and RMSE. Findings suggest that age can be used as deciding factor for the measured traits including BW, BL, HG, WH and RH in both does and bucks. Findings suggest that sex can only be used as a deciding for RL only in the current study. / National Research Foundation (NRF)

由於資訊科技的進步與網路的普及，企業得以收集與儲存大量的資料。使用資訊工具來協助資料處理、資訊擷取、以及產生知識已然變成企業的重要課題之一，所以如何良好運用資料探勘工具成為使用者關注的焦點。由於並非每一個使用者對於資料探勘的原理都有充分的了解，所以如何從探勘工具提供的功能中選用最佳的解決方案並不容易。如果對於探勘結果不滿意而需要調整軟體邏輯，與IT人員的協商溝通卻又曠日費時。 為了解決這個問題，本研究提出一個演算法選擇方法，藉由分析商業應用的內容，來自動對應到特定的資料探勘方法與演算法，讓選擇演算法的過程更為快速、更系統化，提升利用資料探勘工具解決商業問題的效率。 / Due to the information technology improvement and the growth of internet, companies are able to collect and to store huge amount of data. Using data mining technology to aid the data processing, information retrieval and knowledge generation process has become one of the critical missions to enterprise, so how to use data mining tools properly is users’ concern. Since not every user completely understand the theory of data mining, choosing the best solution from the functions which data mining tools provides is not easy. If user is not satisfied with the outcome of mining, communication with IT employees to adjust the software costs lots of time. To solve this problem, a selection model of data mining algorithms is proposed. By analyzing the content of business application, user’s requirement will map to certain data mining category and algorithm. This method makes algorithm selection faster and reasonable to improve the efficiency of applying data mining tools to solve business problems.

Made available in DSpace on 2016-06-02T19:06:09Z (GMT). No. of bitstreams: 1 5554.pdf: 2294386 bytes, checksum: ce6dc6cd7128337c0533ddd23c0bc601 (MD5) Previous issue date: 2013-06-07 / The mining of sequential patterns in data from environmental sensors is a challenging task: the data may show noise and may also contain sparse patterns that are difficult to detect. The knowledge extracted from environmental sensor data can be used to determine climate change, for example. However, there is a lack of methods that can handle this type of database. In order to reduce this gap, the algorithm Incremental Miner of Stretchy Time Sequences with Post-Processing (IncMSTS-PP) was proposed. The IncMSTS-PP applies incremental extraction of sequential patterns with post-processing based on ontology for the generalization of the patterns. The post-processing makes the patterns semantically richer. Generalized patterns synthesize the information and makes it easier to be interpreted. IncMSTS-PP implements the Stretchy Time Window (STW) that allows stretchy time patterns (patterns with temporal intervals) are mined from bases that have noises. In comparison with GSP algorithm, IncMSTS-PP can return 2.3 times more patterns and patterns with 5 times more itemsets. The post-processing module is responsible for the reduction in 22.47% of the number of patterns presented to the user, but the returned patterns are semantically richer. Thus, the IncMSTS-PP showed good performance and mined relevant patterns showing, that way, that IncMSTS-PP is effective, efficient and appropriate for domain of environmental sensor data. / A mineração de padrões sequenciais em dados de sensores ambientais é uma tarefa desafiadora: os dados podem apresentar ruídos e podem, também, conter padrões esparsos que são difíceis de serem detectados. O conhecimento extraído de dados de sensores ambientais pode ser usado para determinar mudanças climáticas, por exemplo. Entretanto, há uma lacuna de métodos que podem lidar com este tipo de banco de dados. Com o intuito de diminuir esta lacuna, o algoritmo Incremental Miner of Stretchy Time Sequences with Post- Processing (IncMSTS-PP) foi proposto. O IncMSTS-PP aplica a extração incremental de padrões sequencias com pós-processamento baseado em ontologia para a generalização dos padrões obtidos que acarreta o enriquecimento semântico desses padrões. Padrões generalizados sintetizam a informação e a torna mais fácil de ser interpretada. IncMSTS-PP implementa o método Stretchy Time Window (STW) que permite que padrões de tempo elástico (padrões com intervalos temporais) sejam extraídos em bases que apresentam ruídos. Em comparação com o algoritmo GSP, o IncMSTS-PP pode retornar 2,3 vezes mais sequencias e sequencias com 5 vezes mais itemsets. O módulo de pós-processamento é responsável pela redução em 22,47% do número de padrões apresentados ao usuário, porém os padrões retornados são semanticamente mais ricos, se comparados aos padrões não generalizados. Assim sendo, o IncMSTS-PP apresentou bons resultados de desempenho e minerou padrões relevantes mostrando, assim, que IncMSTS-PP é eficaz, eficiente e apropriado em domínio de dados de sensores ambientais.