Factor Analysis is a very useful linear algebra technique used for dimensionality reduction. It is also used for data compression and visualization of high dimensional datasets. This technique tries to identify from among a large set of variables, a reduced set of components which summarizes the original data. This is done by identifying groups of variables which have a strong inter correlation. The original variables are transformed into a smaller set of components which have a strong linear correlation. Using several data analysis techniques like Principal Components Analysis (PCA), Factor Analysis, cluster analysis may give insight into the patterns present in the data but may also give different results. The aim of this work is to study the use of Factor Analysis (FA) in capturing the cluster structures from transportation (HIS) data. It is proposed to compare the clustering obtained from original data from that of factor scores. Steps involved in preprocessing the transportation data are also illustrated.

Wireless Mesh networks (WMNs) suffers from abundant security issues because of its dynamic and open communication channels. It is thus risky to formulate an Intrusion Detection System (IDS) that could make out diverse unidentified attacks in the network. This paper intends to propose an Improved Selection of Encircling and Spiral updating position of WO (ISESW) based model for detecting the attacks in WMN systems. The adopted scheme includes two phase's namely, Feature Selection and Classification. Initially, the features (informative features) from the given data are selected using Principal Component Analysis (PCA) model. The selected informative features are then subjected to classification process using Neural Network (NN), where the presence of attacks is classified. To make the detection more accurate, the weights of NN are fine-tuned using the ISESW algorithm, which is the improved version of WOA model. Finally, the superiority of adopted scheme is evaluated over traditional models in terms of varied measures.

Data mining techniques support numerous applications of intelligent transportation systems (ITSs). This paper critically reviews various data mining techniques for achieving trip planning in ITSs. The literature review starts with the discussion on the contributions of descriptive and predictive mining techniques in ITSs, and later continues on the contributions of the clustering techniques. Being the largely used approach, the use of cluster analysis in ITSs is assessed. However, big data analysis is risky with clustering methods. Thus, evolutionary computational algorithms are used for data mining. Though unsupervised clustering models are widely used, drawbacks such as selection of optimal number of clustering points, defining termination criterion, and lack of objective function also occur. Eventually, various drawbacks of evolutionary computational algorithm are also addressed in this paper.

Planning a trip not only depends on the traveling cost, time, and path, but also on the socio-economic status of the traveler. This paper attempts to introduce a new trip planning model that is able to work on real-time data with multiple socio-economic constraints. The proposed trip planning model processes real-time data to extract the relevant socio-economic attributes; later, it mines the most frequent as well as the feasible attributes to plan the trip. The relevance of the socio-economic constraints is defined using correlations, whereas the frequent as well as the feasible attributes are mined through the sequential pattern mining approach. Real-time travel information of about 38,303 trips was acquired from the Indian city of Hyderabad, and the proposed model was subjected to experimentation. The proposed model maintained a substantial trade-off between multiple performance metrics, though the trip mean model performed statistically.

Abstract. In recent past, tremendous work has been done to find optimal number of clusters at run time for partitional clustering algorithms. Various Evolutionary Computation techniques have been used by researchers to evolve most appropriate number of clusters for different clustering problems. In this paper, we attempt to apply a new variant of adaptive differential evolution technique on a real world data set to find optimal number of clusters at runtime. The DCADE algorithm has been applied on Home Interview Survey (HIS) data related to a Transportation Project. Later clusters are formed and analyzed which are in accordance with the domain expert.