These spatial clustering methods can be classified into four categories: partitioning method, hierarchical method, density-based method and grid-based method. The grid-based clustering algorithm, which partitions the data space into a finite number of cells to form a grid structure and then performs all clustering operations to group similar spatial objects into classes on this obtained grid st...

Grid partitioning for input space results in the exponential rise number of rules adaptive network-based fuzzy inference system (ANFIS) and patch learning (PL) as features increases, thus resulting huge computational load deteriorating its interpretability. An improved PL (iPL) is put forward training each sub-fuzzy to overcome rule-explosion problem. In iPL, done using c-means (FCM) clustering...

Given d-dimensional N data items and a maximum branching factor Bfmax, packing is partitioning Bfmax amount of data and storing it in a disk page. The range query performance of packing is highly dependent on the methods by which data is partitioned. Thus, partitioning data is the main problem tackled in our work. We suggest two extreme cases of partitioning methods: grid-like balanced and onio...

This paper reviews the various control crowbar methods associated with doubly-fed induction generator (DFIG) wind energy system (WES) during power faults. The are designed to improve fault ride-through (FRT) capability of DFIGs based on WESs. adaptive neural-fuzzy inference (ANFIS) is developed detect conditions and protection techniques. proposed ANFIS technique detects measurement three phase...

DBSCAN is widely used in various fields, but it requires computational costs similar to those of re-clustering from scratch update clusters when new data inserted. To solve this, we propose an incremental density-based clustering method that rapidly updates by identifying advance regions where cluster will occur. Also, through extensive experiments, show our provides results DBSCAN.

In the present study, the adaptive neuro-fuzzy inference system (ANFIS) is developed for the prediction of effective thermal conductivity (ETC) of different fillers filled in polymer matrixes. The ANFIS uses a hybrid learning algorithm. The ANFIS is a class of adaptive networks that is functionally equivalent to fuzzy inference systems (FIS). The ANFIS is based on neuro-fuzzy model, trained wit...

This paper investigates the effectiveness of four different soft computing methods, namely radial basis neural network (RBNN), adaptive neuro fuzzy inference system (ANFIS) with subtractive clustering (ANFIS-SC), ANFIS with fuzzy c-means clustering (ANFIS-FCM) and M5 model tree (M5Tree), for predicting the ultimate strength and strain of concrete cylinders confined with fiber-reinforced polymer...

Prediction of student’s performance is potentially important for educational institutions to assist the students in improving their academic performance, and deliver high quality education. Developing an accurate student’s performance prediction model is challenging task. This paper employs the Adaptive NeuroFuzzy Inference system (ANFIS) for student academic performance prediction to help stud...