ABSTRACT :
Research on articulating the design space in computational generative systems is ongoing, to overcome the issue of possible overwhelming multiplicity and redundancy of emerging design options. The article contributes to this line of research of design space articulation, in order to facilitate designers’ successful exploration in computational design. We have recently developed a method for shape clustering using K-Medoids, a machine learning-based strategy. The method performs clustering of similar design shapes and retrieves a representative shape for each cluster in 2D grid-based representation. In this paper, we present a progress in our project where the method has been applied to a new test case, and empirically verified using clustering evaluation methods. Our clustering evaluation results show comparable accuracy when assessed against an external study and provide insight into the evaluation criteria for machine learning methods, as presented in the paper.
EXISTING SYSTEM :
Articulating design solutions produced in computational generative schemes has only recently been addressed (Rodrigues et al., 2017; Brown & Mueller, 2019). Effective exploration of the design problem requires a portrayal of corresponding geometry of designs under analysis (Turin et al., 2016). The study of Brown and Mueller reviews different diversity metrics used in generative design protocols for design space articulation (2019). Seeking design diversity among the possible design alternatives is important to avoid obtaining, or simulating, repeated and similar candidates and to enhance generative mechanisms to ensure that “the results they produce are diverse enough to be interesting to designers” (Rodrigues et al., 2017, p. 2). In a prior work within this research project, a diversity measure was developed to condense the design set into a highly diverse one (Yousif et al., 2017). In continuing experimentation with design diversity, it became obvious that clustering methods have the capability to lead not only to a highly diverse set of designs, but also retain and organize all designs. Therefore, clustering techniques were investigated.
DISADVANTAGES OF EXISTING SYSTEM :
1) Less accuracy
2)low Efficiency
PROPOSED SYSTEM :
The methodology of this work involved an extensive literature study, experimenting and prototyping, and testing and validation. These methods have led to the development of a new shape clustering method, the (SC-KM). Developing the method, the protocol included employing a grid-based descriptor, formulating a shape difference finding method, and implementing the K-Medoids clustering algorithm. In our previous publications, the SC-KM method was fully described (Yousif & Yan, 2019a, b). Experimenting and prototyping were performed in the Rhino/Grass hopper R environment. For the grid-based shape generation and description, modelling was pursued, using visual programming, in addition to customized programs written in Grass hopper R Python, and C# languages. For the SC-KM, a package of algorithmic set was developed, primarily using the GH C Python tool (Abdel Rahman, 2017) that allows communicating with the Python environment, and incorporating the scientific libraries and modules. For explaining the test-case application and evaluation, the focus of this paper, there is a need to concisely describe the SC-KM method and introduce its algorithms.
For shape description, a typical grid-based approach was employed to define the shape characteristics. The shape difference finding method we developed started with investigating the distance-based diversity measure (Toffolo & Benini, 2003). Since the dataset in our case is a set of shapes, we needed a method applied to multidimensional space such as the architectural design space. As such, we formulated two sets of algorithms: (1) pair-wise shape difference and the Hungarian algorithm, and (2) K-Medoids clustering.
ADVANTAGES OF PROPOSED SYSTEM :
1) High accuracy
2)High efficiency
SYSTEM REQUIREMENTS
SOFTWARE REQUIREMENTS:
• Programming Language : Python
• Font End Technologies : TKInter/Web(HTML,CSS,JS)
• IDE : Jupyter/Spyder/VS Code
• Operating System : Windows 08/10
HARDWARE REQUIREMENTS:
Processor : Core I3
RAM Capacity : 2 GB
Hard Disk : 250 GB
Monitor : 15″ Color
Mouse : 2 or 3 Button Mouse
Key Board : Windows 08/10
