Data Mining - Concepts and Techniques.

Social Network Analysis

Numerous number of tourism attractions along with a huge amount of information about them on web and social platforms have made the decision-making process for selecting and visiting them complicated. In this regard, the tourism recommendation systems have become interesting for tourists, but challenging for designers because they should be able to provide personalized services. This paper introduces a tourism recommendation system that extracts users' preferences in order to provide personalized recommendations. To this end, users' reviews on tourism social networks are used as a rich source of information to extract their preferences. Then, the comments are preprocessed, semantically clustered, and sentimentally analyzed to detect a tourist's preferences. Similarly, all users aggregated reviews about an attraction are utilized to extract the features of these points of interest. Finally, the proposed recommendation system, semantically compares the preferences of a user with the features of attractions to suggest the most matching points of interest to the user. In addition, the system utilizes the vital contextual information of time, location, and weather to filter unsuitable items and increase the quality of suggestions regarding the current situation. The proposed recommendation system is developed by Python and evaluated on a dataset gathered from TripAdvisor platform. The evaluation results show that the proposed system improves the f-measure criterion in comparison with the previous systems.

Tourism recommendation system based on semantic clustering and sentiment analysis

Kidney-inspired algorithm for optimization problems

Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm, inspired by the cooperative behavior and chasing style of Harris’ Hawks in nature called surprise pounce. HHO demonstrated promising results compared to other optimization methods. However, HHO suffers from local optima and population diversity drawbacks. To overcome these limitations and adapt it to solve feature selection problems, a novel metaheuristic optimizer, namely Chaotic Harris Hawks Optimization (CHHO), is proposed. Two main improvements are suggested to the standard HHO algorithm. The first improvement is to apply the chaotic maps at the initialization phase of HHO to enhance the population diversity in the search space. The second improvement is to use the Simulated Annealing (SA) algorithm to the current best solution to improve HHO exploitation. To validate the performance of the proposed algorithm, CHHO was applied on 14 medical benchmark datasets from the UCI machine learning repository. The proposed CHHO was compared with the original HHO and some famous and recent metaheuristics algorithms, containing Grasshopper Optimization Algorithm (GOA), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Butterfly Optimization Algorithm (BOA), and Ant Lion Optimizer (ALO). The used evaluation metrics include the number of selected features, classification accuracy, fitness values, Wilcoxon’s statistical test ( $P$ -value), and convergence curve. Based on the achieved results, CHHO confirms its superiority over the standard HHO algorithm and the other optimization algorithms on the majority of the medical datasets.

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An Improved Harris Hawks Optimization Algorithm With Simulated Annealing for Feature Selection in the Medical Field

Fuzzy job-shop scheduling problems: A review

Data aggregation from different databases into a data warehouse creates multidimensional data such as data cubes. With regard to the 3D structure of data, data cube clustering has significant challenges to perform on data cube. In this paper, new preprocessing techniques and a novel hybridization of DBSCAN and fuzzy earthworm optimization algorithm (EWOA) are proposed to solve the challenges. Proposed preprocessing consists of an assigned address to each cube cell and dimension move to create a related 2D data from the data cube and new similarity metric. The DBSCAN algorithm, as a density-based clustering algorithm, is adopted based on both Euclidean and newly proposed similarity metric, which are called DBSCAN1 and DBSCAN2 for the related 2D data. A new hybridization of the EWOA and DBSCAN is proposed to improve the DBSCAN, and it is called EWOA–DBSCAN. Also, to dynamically tune parameters of EWOA, a fuzzy logic controller is designed with two fuzzy group rules of Mamdani (EWOA–DBSCAN-Mamdani) and Sugeno (EWOA–DBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN’s parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the proposed algorithms are compared with DBSCAN1 and GA-DBSCAN1, GA-DBSCAN1-Mamdani and GA-DBSCAN1-Sugeno. The experimental results, consisting of 20 runs, indicate that the proposed ideas achieved their targets.

A new hybridization of DBSCAN and fuzzy earthworm optimization algorithm for data cube clustering

The job-shop scheduling problem (JSSP) is an important decision facing those involved in the fields of industry, economics and management. This problem is a class of combinational optimization problem known as the NP-hard problem. JSSPs deal with a set of machines and a set of jobs with various predetermined routes through the machines, where the objective is to assemble a schedule of jobs that minimizes certain criteria such as makespan, maximum lateness, and total weighted tardiness. Over the past several decades, interest in meta-heuristic approaches to address JSSPs has increased due to the ability of these approaches to generate solutions which are better than those generated from heuristics alone. This article provides the classification, constraints and objective functions imposed on JSSPs that are available in the literature. Keywords—Job-shop scheduling, classification, constraints, objective functions.

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Job Shop Scheduling: Classification, Constraints and Objective Functions

Multi-dimensional data, such as data cube, are constructed based on aggregating data in data warehouses and it requires to analyze with high flexibility. Also, clustering, which is an unsupervised pattern recognition analysis, has significant challenges to perform on data cube. In this paper, two new drafts of density-based clustering methods are designed to recognize unsupervised patterns of the data cube. In the first draft, DBSCAN clustering is hybridized by genetic algorithm and called the Improved DBSCAN (IDBSCAN). The motivation of designing the IDBSCAN optimizes the DBSCAN’s parameters by a meta-heuristic algorithm such as GA. The second draft, which is called the Soft Improved DBSCAN (SIDBSCAN), is designed based on fuzzy tuning parameters of the GA in the IDBSCAN. The fuzzy tuning parameters are performed with two fuzzy groups rules of Mamdani (SIDBSCAN-Mamdani) and Sugeno (SIDBSCAN-Sugeno), separately. These ideas are proposed to present efficient and flexible unsupervised analysis for a data cube by utilizing a meta-heuristic algorithm to optimize DBSCAN’s parameters and increasing the efficiency of the idea by applying dynamic tuning parameters of the algorithm. To evaluate the efficiency, the SIDBSCAN-Mamdani and the SIDBSCAN-Sugeno are compared with the IDBSCAN and the DBSCAN. The experimental results, consisted of 20 times running, indicate that the proposed ideas achieved to their targets.

/pdf/data-cube-clustering-with-improved-dbscan-based-on-fuzzy-5g5m8gpn9m.pdf

Data Cube Clustering with Improved DBSCAN based on Fuzzy Logic and Genetic Algorithm

With the increasing popularity of location-based social networks (LBSNs), community detection has emerged as an important and practical issue. One of the main shortcomings of the previous methods is that cluster’s centers have been selected randomly in clustering the communities; therefore, different results are obtained in each execution. This paper proposes an intelligent method to detect overlapping communities in LBSNs with respect to user check-ins and the attributes of venues and users. In the proposed approach, clustering is performed on the two-segment user-venue edges. The advantage of this approach is the use of subtractive clustering method, which determines cluster centers with a probability, based on the potential of each data point. Furthermore, the specified fuzzy rules have a significant role in determining the appropriate number of cluster centers. The paper concentrates on the data obtained from the Foursquare LBSN. The empirical results of the proposed approach are compared with the previous approach to clustering edges in multi-attribute and multi-mode (M2-clustering) algorithm, and a significant improvement is observed in the cost function of the community detection.

Majid Abdolrazzagh-Nezhad

Papers

Fuzzy job-shop scheduling problems: A review

A new hybridization of DBSCAN and fuzzy earthworm optimization algorithm for data cube clustering

Job Shop Scheduling: Classification, Constraints and Objective Functions

Data Cube Clustering with Improved DBSCAN based on Fuzzy Logic and Genetic Algorithm

Detecting overlapping communities in LBSNs by fuzzy subtractive clustering