Abstract-Spatio-Temporal data is related to many of the issues around us such as satellite images, weather maps, transportation systems and so on. Furthermore, this information is commonly not static and can change over the time. Therefore the nature of this kind of data are huge, analysing data is a complex task. This research aims to propose an intermediate data model that can represented suitable for Spatio-Temporal data and performing data mining task easily while facing problem in frequently changing the data. In order to propose suitable data model, this research also investigate the analytical parameters, the structure and its specifications for Spatio-Temporal data. The concept of proposed data model is inspired from the nature of hair which has specific properties and its growth over the time. In order to have better looking and quality, the data is needed to maintain over the time such as combing, cutting, colouring, covering, cleaning etc. The proposed data model is represented by using mathematical model and later developed the data model tools. The data model is developed based on the existing relational and object-oriented models. This paper deals with the problems of available SpatioTemporal data models for utilizing data mining technology and defines a new model based on analytical attributes and functions.Keywords-hair data model; spatio-temporal data models; data warehouse model.

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Spatio-temporal data are complex in terms of number of attributes for spatial and temporal values, and the data are changing towards time. Traditional method to mining the spatio-temporal data is the fact that the data is stored in data warehouse in un-normalization form as union of spatial and temporal data know as tabular data warehouse. A Hair-Oriented Data Model (HODM) has been proved as a suitable data model for spatio-temporal data. It has reduced the file size and decreased query execution time. The spatio-temporal data stored using the HODM known as Hair-Oriented Data warehouse. However, this paper aims to presents a method to develop spatio-temporal data mining model using the Hair-Oriented data warehouse. The Hair-Oriented data model also provide with various functions for easy maintenance on spatio-temporal data warehouse. Experiment conducted using Climate-change spatio-temporal data set benchmark. Two Climate-change spatio-temporal models been developed using regression and knearest neighbor techniques. The performance of the Hair-Oriented Data Warehouse is evaluated by comparing its performance with traditional tabular data warehouse. The result shows that developing data mining spatio-temporal model using Hair-Oriented data warehouse is faster compare using the tabular data warehouse, therefore it can be concluded that the Hair-Oriented Data Model is suitable for Spatio-temporal data mining.

Data Mining is a method that can be used to analyze large amount of data and produce useful information. In this study, clustering which is one of data mining tasks is used to clustered machine based on the injection moulding data. This paper is the first documented results on the optimization of Injection Moulding via Data Mining. Powder injection moulding is a process to produce near net shape with intricate part in mass production. This work focus on the optimization of injection molding process with combination of fine, coarse and bimodal water atomized SS 316L powder particles. The parameters involved in the optimization are injection pressure, injection temperature, mould temperature, holding pressure, injection rate, holding time, powder loading, cooling time and particle size. These variables are based on the defect score, green density and green strength. The key influencer report shows that the most influencing factors are injection rate, holding pressure as well as mould temperature where defect score lower than 2.4 can be achieved. The density higher than 5.34g/cm3 is also influenced most by the mould temperature. The result also shows that the optimize condition can be achieved by using bimodal particle. Injection rate and mould temperature gives the highest impact on the defect score and green strength value. While highest green density is significantly affected by powder loading and injection pressure.