Extraction of Characteristic Subgraph Patterns with Support Threshold from Databases of Floor Plans The rental price of apartments is determined by various factors such as building age, floor space, and so forth. Although room layouts, or connection structures among rooms, are considered to affect the price, it is not obvious that what kind of layout has a strong influence. Identification of characteristic layouts on the price has a large impact on property lenders to determine appropriate rental price as well as on property borrowers to judge the reasonableness. In this paper, a framework to extract characteristic patterns on room layouts is proposed based on advanced techniques in frequent subgraph mining. Floor plans are represented as graphs, and class labels are given based on the estimated residuals by certain basic attributes. For the set of labeled floor plans, we develop a new class of contrast pattern having support threshold, under new definitions of support value of a pattern in a floor plan. In addition, we extract decision lists and decision sets consisting of significant subgraph patterns with thresholds by using methods in interpretability research of machine learning. The effectiveness of the proposed framework is evaluated in preliminary experiments using a real world dataset.

An Integrated Model for Urban Subregion House Price Forecasting: A Multisource Data Perspective Urban housing price is widely accepted as an economic indicator of both business and research interest in urban computing. In this work, we propose an effective and finegrained model for urban subregion housing price predictions. Compared to existing works, our proposal improves the forecasting granularity from citylevel to milelevel in spite of data sparsity and complex factors. The finegrained housing price forecasting has the potential to support a broad scope of applications, ranging from urban planning to housing market recommendations. To achieve that, in this paper, we propose a novel integrated framework, FTD_DenseNet, which incorporates more social and economic features and makes full use of alllevel spatiotemporal features. Specifically, the Kalman Filterbased future expection is firstly involved as an influence factor in our model. Extensive empirical studies on real data show the effectiveness of our proposals.