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Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a density based dynamic data clustering algorithm for clustering incremental dataset and compare its performance with full run of normal DBSCAN, Chameleon on the dynamic dataset. Most of the clustering algorithms perform well and will give ideal performance with good accuracy measured with clustering accuracy, which is calculated using the original class labels and the calculated class labels. However, if we measure the performance with a cluster validation metric, then it will give another kind of result. Results: This study addresses the problems of clustering a dynamic dataset in which the data set is increasing in size over time by adding more and more data. So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI), Davies-Bouldin index (DB) as the cluster validation metric and as well as time taken for clustering. Conclusion: In this study, we have successfully implemented and evaluated the proposed density based dynamic clustering algorithm. The performance of the algorithm was compared with Chameleon and DBSCAN clustering algorithms. The proposed algorithm performed significantly well in terms of clustering accuracy as well as speed.

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We assessed the status of pig farming and its contribution to the livelihoods of rural households in the study area. Snowball sampling was used to sample respondents (n=533), and data was collected using a structured questionnaire. Data was analysed using descriptive statistics and ANOVA. The majority (62%) of respondents were females over 46 years of age. Most respondents (62%) had no schooling or had only attended primary school. Male respondents reared significantly (p < 0.05) larger herds. Respondents who had secondary school education tended to rear significantly (p < 0.05) larger herds. The few respondents (1%) who held formal jobs tended to rear significantly (p < 0.05) larger herds. Livestock contributed less (10%) to household income compared to government grants that contributed 47.3%. More than half of respondents (63%) kept pigs for household consumption, while only 33% reared pigs as a source of income. Respondents still reared local pig breeds, and fed their pigs predominantly on swill (81%). Very few respondents had been exposed to training on pig rearing. Most pigs are slaughtered locally and sold mainly at social grant pay points on days when grants are paid out. The potential of pigs to support rural households has not been fully exploited in the study area. There is a need for skills transfer to limit the risks associated with feeding swill and poor animal husbandry practices. Meat inspection services are needed to limit the risk of diseases like cysticercosis.