Habiba Drias

University of Science and Technology Houari Boumediene | usthb

Clustering is still one of the most common unsupervised learning techniques in data mining since it allows the discovery of meaningful and interesting patterns, knowledge, rules and associations from large-scale datasets. K-medoids, a variant of K-means, is a popular clustering method that attempts to find the optimal combination of K medoids from...

In this paper, we are proposing two novel swarm intelligence approaches for solving discrete problems. Two different continuous evolutionary algorithms inspired by the behaviour of elephants, namely elephant herding optimization and elephant swarm water search algorithm, are studied and analysed in order to propose discrete versions of the latter c...

Soil health affects soil functions, food production, and climate change. Given the importance of understanding our soils, we review the most recent research and challenges of various Digital Soil Mapping (DSM) techniques from 2017 to the present (2022). We outline the steps of the DSM process. From Data Acquisition, where we report the most used da...

In this research, the path planning and obstacle avoidance problem for Swarm Robotics is addressed. Our contribution consists in a Discrete adaptation of the well-known Input-Space Sampling method (DISS). The DISS is integrated into a Swarm Intelligence approach, named Discrete Elephant Herding Optimization (DEHO) that must be embedded into autonom...

In this paper, a new swarm intelligence algorithm based on orca behaviors is proposed for problem solving. The algorithm called artificial orca algorithm (AOA) consists of simulating the orca lifestyle and in particular the social organization, the echolocation mechanism, and some hunting techniques. The originality of the proposal is that for the...

In this paper, Multi-Objective Harris Hawks Optimization (MOHHO) is proposed based on strengthened dominance relation (SRD) to solve multi-objective optimization problems. Specifically, MOHHO uses an additional population archive to store the best non-dominated solutions generated so far by the exploration process at the aim to maintain the elitism...

In this article, we present a novel information access approach inspired by the information foraging theory (IFT) and elephant herding optimization (EHO). First, we propose a model for information access on social media based on the IFT. We then elaborate an adaptation of the original EHO algorithm to apply it to the information access problem. The...

In this paper, the quantum technology is exploited to empower the OPTICS unsupervised learning algorithm, which is a density-based clustering algorithm with numerous applications in the real world. We design an algorithm called Quantum Ordering Points To Identify the Clustering Structure (QOPTICS) and demonstrate that its computational complexity o...

This paper presents an overview of the key points that help to build a system for detecting and analyzing afterwards the climate change in the Maghreb region. Our main goal is to propose a design and an implementation of a meteorology data warehouse built from multiple sources and intended to end-users for making prediction and effective decision....

In this paper, a novel Deep Self-Learning Approach applied to Artificial Orca Algorithm and based on mutation operators is proposed. The idea of contribution comes back to the stagnation that Swarm Intelligence Algorithms are facing. The proposed framework is based on two mutation operators known as Cauchy and Gaussian operators. To evaluate the pr...

Emergencies must be taken into account very quickly (in real-time). With the arrival of the COVID-19 pandemic, responding to COVID-19 emergency calls has become a challenge for rescuers, especially for countries that must frequently receive tourists or pilgrims, as in the case of Saudi Arabia. For this purpose, the present paper proposes artificial...

In this article, the paradigm of machine culture as an extension to machine intelligence is introduced. This new concept is modelled based on animal intelligence and culture. The example of orca intelligence and culture is considered as orcas possess in addition to skills allowing them to reach preys, the ability to transmit their culture from gene...

Unsupervised machine learning and especially unshaped clustering approaches like Density-Based Spatial Clustering of Applications with Noise (DBSCAN) remain at the center of researchers’ attention. Many DBSCAN improvements have been recently proposed, but none of them involve the data heterogeneity that we may find in real-life problems. To tackle...

Studies exploring the use of artificial intelligence (AI) and machine learning (ML) are knowing an undeniable success in many domains. On the other hand, quantum computing (QC) is an emerging field investigated by a large expanding research these last years. Its high computing performance is attracting the scientific community in search of computin...

This article presents a data mining study carried out on social media users in the context of COVID-19 and offers four main contributions. The first one consists in the construction of a COVID-19 dataset composed of tweets posted by users during the first stages of the virus propagation. The second contribution offers a sample of the interactions b...

Detecting hiding communities is considered as a main topic in complex networks. In this article, we propose a multi‐swarm elephant herding optimization (EHO) algorithm to uncover community structures in complex environments. It adapts EHO algorithm to community detection problem. EHO algorithm relies on two procedures which are updating clan proced...

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence approaches, Swarm Robotics became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and unknown en...

In this paper, a very interesting species in its behaviour, social organization and also hunting/foraging techniques is mimicked in a new swarm intelligence algorithm. Several observations of Orcas in their living environment allows to model this species in a swarm optimization algorithm called Artificial Orca Algorithm (AOA). The algorithm propose...

During the last decade, Genetic Algorithms have known an increasing interest, especially in Machine Learning and Deep Learning fields, used to accelerate the learning process of Artificial Neural Networks to solve a certain problem. Our contribution consists in using Genetic Algorithm to optimize the parameter tuning of the swarm intelligence-based...

Nowadays, Swarm Intelligence (SI) is considered as one of the most promising class of soft computing and bio-inspired algorithms for solving complex optimization problems. Particle Swarm Optimization (PSO) is by far the most popular and widely used swarm intelligence algorithms because of its efficient simplicity, high flexibility, and fast converg...

The terms data, information and knowledge should not be treated as synonyms in any context. In fact, a hierarchical order between these entities exists where data become information and information become knowledge. Massive amounts of data are analysed everyday in order to extract valuable knowledge to support decision making. However, the size of...

In this paper, a new Information Foraging approach based on Elephant Herding Optimization (EHO) is proposed and tested on social media. We adapted the original EHO algorithm and combined it with the information foraging theory. In order to test our approach, we constructed a dataset containing more than one million tweets collected during the secon...

Multi-agent systems as an active research axis in artificial intelligence (AI) have known a tremendous progress these last decades. Their main strength resides in the cooperation that makes agents intelligent as a group. The paradigm of multi-robot systems derives from the agent technology field. In this paper, we are interested in developing a mul...

In the era of autonomous robots, multi-targets search methods inspired researchers to develop adapted algorithms to robot constraints, and with the rising of Swarm Intelligence (SI) approaches, Swarm Robotics (SRs) became a very popular topic. In this paper, the problem of searching for an exponentially increasing number of targets in a complex and...

Metaheuristics and especially Swarm Intelligence represents one of the mostly used aspect of Artificial Intelligence. In fact, these algorithms are exploited in several domains from theoretical problem solving to air traffic management. The evaluation of such methods is defined by the quality of solution they provide or effectiveness and the spent...

UNSTRUCTURED A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of t...

In this paper, a new swarm intelligence algorithm based on orcas behaviors is proposed for problem solving. The algorithm called Orcas Algorithm (OA) consists in simulating the orcas life style and in particular their social organization, their echolocation practice and their hunting techniques. The experimental study we conducted tested the design...

A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of...

Currently, robot technology plays a crucial role in our modern life. Its great favor over humanity was not limited to the industrial domain, but also on the social one. Social robots become equipped with an artificial culture that allows them to interact with humans. Although the great importance of this technology, some misdeeds can create a real...

In this paper, for the first time, a novel discretization scheme is proposed aiming at enabling scalability but also at least three other strong challenges. It is based on a Left-to-Right (LR) scanning process, which partitions the input stream into intervals. This task can be implemented by an algorithm or by using a generator that builds automati...

The originality of this work resides into the exploitation of data mining techniques for problem solving. Two major phases define this work. The first one is to determine the clustering technique that best suits each SAT instance based on the distribution of the later. The clustering technique is then applied to reduce the complexity of each instan...

Query expansion (QE) is one of the most effective techniques to enhance the retrieval performance and to retrieve more relevant information. It attempts to build more useful queries by enriching the original queries with additional expansion terms that best characterize the users' information needs. In this chapter, the authors propose a new correl...

Clustering is by far the most commonly used unsupervised data mining techniques for discovering interesting knowledge and patterns. It aims to group a set of data objects into clusters that are coherent internally but basically different from each other. In this work, we involve clustering algorithms in Information Retrieval (IR) to strengthen the...

Discovering communities in complex environment is an interesting topic for many scientists. It becomes the mainstream issue in different research fields such as the web mining, biological networks and social networks. In this study, we propose a multi swarm version of Bee Swarm Optimization (BSO) algorithm for community detection problem with local...

Nowadays, artificial intelligence makes a great success in our modern social life. The human should be prepared to be able to live with social robots that can provide him comfort and help in solving complex processes. Extending the use of robot’s technology is certainly desirable, but preventing certain catastrophes from the misdeeds of artificial...

Nowadays, the industrial company are attached with a very competitive and Highly dynamic Environment that require a rapid reaction in order To avoid the accumulation of defects in the execution plans and to ensure a better control of the manufacturing and supply plans while communicating the real-time situation to the customers vis-à-vis their orde...

Over the last decade, social media have dominated our lives. The exploding number of data produced by these platforms triggered a wave of research works that mainly focus on the storage and analysis of this data. In this paper, we propose an original information warehouse architecture for the storage and analysis of social media information. A mult...

Purpose This paper is an extended version of Hireche and Drias (2018) presented at the WORLD-CIST’18 conference. The major contribution, in this work, is defined in two phases. First of all, the use of data mining technologies and especially the tools of data preprocessing for instances of hard and complex problems prior to their resolution. The a...

In this paper, we propose a novel supervised discretization method namely LR-SDiscr. It is based on a Left to Right (LR) scanning technique, which partitions automatically the input stream into intervals. Its originality resides in the fact it handles both merging and division operations in the same process, and hence it benefits of the use of a la...

Data and information are different constructs and Data become information when they supply a content from several elements, generating thus a meaning. From this observation, in this paper, we introduce the paradigm of information warehousing and explain our motivation. We propose a generic and original information warehouse architecture for the sto...

Data mining techniques and nature-inspired algorithms are currently among the most frequently used soft computing techniques for knowledge discovery, optimization, and computational intelligence. In this paper, we propose the application of both data mining techniques and nature-inspired algorithms to overcome the problem of generating the optimum...

Uses1 of association rules for databases exploration and predictions is one of the best proven methods. However, generating association rules is mainly based on extracting frequent patterns which represents the useful information in source database. Apriori is an effective algorithm that generates those items, but it consumes a huge processing time...

In this paper, we introduce the paradigm of information warehousing and provide a generic information warehouse infrastructure for social media to enable the storage and analysis of massive information volumes generated by users daily through these platforms. We illustrate the implementation of the proposed framework in the case of Twitter by givin...

In this paper, we explore data mining techniques for preprocessing Satisfiability problem -SAT- instances, reducing the complexity of the later and allowing an easier resolution. Our study started with the exploration of the variables distribution on clauses, where we defined two kinds of distribution. The first distribution represents a space wher...

The difficulty to predict the human’s behavior has caused the need to learn cultural differences between peoples. Although culture is one of the concepts that are difficult to define, best learning these differences depends on the culture modeling quality. In this paper, a new culture modeling is proposed to facilitate the learning process. This mo...

Numerical association rule mining problem attracts the attention of researchers because of the various applications and its importance in our world with the fast growth of the stored data. ARM is computationally very expensive because the number of rules grows exponentially as the number of items in the database increases. Generally, ARM is more co...

Numerical association rule mining problem attracts the attention of researchers because of the various applications and its importance in our world with the fast growth of the stored data. ARM is computationally very expensive because the number of rules grows exponentially as the number of items in the database increases. Generally, ARM is more co...

Because of users’ growing utilization of unclear and imprecise keywords when characterizing their information need, it has become necessary to expand their original search queries with additional words that best capture their actual intent. The selection of the terms that are suitable for use as additional words is in general dependent on the degre...

Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficie...

The selection of information sources in a distributed information retrieval environment remains a critical issue. In this context, it is known that a distributed information retrieval system consists of a huge number of sources. Ensuring retrieval effectiveness is to search only sources which are likely to contain relevant information for a query....

During the last few years, it has become abundantly clear that the technological advances in information technology have led to the dramatic proliferation of information on the web and this, in turn, has led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play an essential rol...

Association rule mining problem attracts the attention of researchers inasmuch to its importance and applications in our world with the fast growth of the stored data. Association rule mining process is computationally very expensive because rules number grows exponentially as items number in the database increases. However, Association rule mining...

Data mining is aimed to extract hidden acknowledge from large dataset, in order to exploit it for predicting future trends and make decisions. Extracting meaningful and useful candidate optimally is handled by several algorithms, mainly those based on exploring incoming data, which can lose information. To address this issue, this paper proposes an...

In this paper, we present a Health Check process (HC) based on artificial neural network (ANN). Our approach aim is to detect Incremental Apriori deviation (IncA) proposed in previous work used in order to minimize processing time and explore new incoming data only. HC process use germinated infrequent items and generated frequent itemset to run co...

We address the problem of information sources selection in a context of a large number of distributed sources. We formulate the sources selection problem as a combinatorial optimization problem in order to yield the best set of relevant information sources for a given query. We define a solution as a combination of sources among a huge predefined s...

The Study of complex networks topology has triggered the interest of many scientists in recent years. It has been widely used in different fields such as protein function prediction, web community mining and link prediction in many areas. This paper purports at proposing an algorithm based on the BSO (bee swarm optimization) for community detection...

In this paper, we propose an efficient multiple classifier system for Arabic handwritten words recognition. First, we use Chebyshev moments (CM) enhanced with some Statistical and Contour-based Features (SCF) for describing word images. Then, we combine several classifiers integrated at the decision level. We consider the multilayer perceptron (MLP...

With the increasing amount of medical data available on the Web, looking for health information has become one of the most widely searched topics on the Internet. Patients and people of several backgrounds are now using Web search engines to acquire medical information, including information about a specific disease, medical treatment or profession...

This paper presents a novel approach for solving the Satisfiability problem by reducing its complexity using a “divide and conquer” version of the Apriori algorithm. First, the problem’s instance is divided into two, or more if necessary, sub-instances from which most frequent used variables are extracted using the Apriori algorithm. These most fre...

One of the most successful techniques to improve the retrieval effectiveness and overcome the shortcomings of search engines is Query Expansion (QE). Despite its effectiveness, QE still suffers from drawbacks that have limited its deployment as a standard component in search systems. Its major weakness is the computational cost, especially for larg...

Bat algorithm is a new nature-inspired metaheuristic optimization algorithm introduced by Yang in 2010, especially based on echolocation behavior of microbats when searching their prey. Firstly, this algorithm is used to solve various continuous optimization problems. Clustering remains one of the most difficult challenges in data mining. In this p...

Association Rule Mining (ARM) can be considered as a combinatorial problem with the purpose of extracting the correlations between items in sizeable datasets. The numerous polynomial exact algorithms already proposed for ARM are unadapted for large databases and especially for those existing on the web. Assuming that datasets are a large space sear...

k-means and k-medoids have been the most popular clustering algorithms based on partitioning for many decades. When using heuristics such as Lloyd’s algorithm, k-means is easy to implement and can be applied on large data sets. However, it presents drawbacks like the inefficiency of the used metric, the difficulty of the choice of the input k and t...

The difficulty of disambiguating the sense of the incomplete and imprecise keywords that are extensively used in the search queries has caused the failure of search systems to retrieve the desired information. One of the most powerful and promising method to overcome this shortcoming and improve the performance of search engines is Query Expansion,...

Association rules mining has attracted a lot of attention in the data mining community. It aims to extract the interesting rules from any given transactional database. This paper deals with association rules mining algorithms for very large databases and especially for those existing on the web. The numerous polynomial exact algorithms already prop...

This paper provides evolutionary approaches in order to extract comprehensible and accurate classification rules. Indeed to construct a model of classification tone must extract not only accurate rules but comprehensible also, to help the human interpretation of the model and the decision make process. In this paper the authors describe a purely ge...

Abstract. Frequent patterns mining is one of the most important data mining techniques, used for extracting information and knowledge from ordinary data. In this paper, we are interested by improving Apriori, which is one of the most used algorithm for extracting frequent patterns. First, we propose some enhancements for the Apriori algorithm. Then...

Community structure identification in complex networks has been an important research topic in recent years. In this paper, a new between-ness centrality algorithm with local search called BCALS in short, is proposed as an effective optimization technique to solve the community detection problem with the advantage that the number of communities is...

Query expansion (QE) has long been suggested as an effective way to improve the retrieval effectiveness and overcome the shortcomings of search engines. Notwithstanding its performance, QE still suffers from limitations that have limited its deployment as a standard component in search systems. Its major drawback is the retrieval efficiency, especi...

Query expansion (QE) has long been suggested as an effective way to improve the retrieval effectiveness and overcome the shortcomings of search engines. Notwithstanding its performance, QE still suffers from limitations that have limited its deployment as a standard component in search systems. Its major drawback is the retrieval efficiency, especi...

Unlike the previous works where detecting communities is performed on large graphs, our approach considers textual documents for discovering potential social networks. More precisely, the aim of this paper is to extract social communities from a collection of documents and a query specifying the domain of interest that may link the group. We propos...

k-means and k-medoids have been the most popular clustering algorithms based on partitioning for many decades. When using heuristics such as Lloyd’s algorithm, k-means is easy to implement and can be applied on large data sets. However, it presents drawbacks like the inefficiency of the used metric, the difficulty of the choice of the input k and t...

Association rule mining (ARM) is well-known issue in data mining. It is a combinatorial optimization problem purpose to extract the correlations between items in sizable data-sets. According to the literature study, bio-inspired prove their efficiency in term of time, memory and quality of generated rules. This paper investigates multi-population c...

Data mining is the process of extracting useful knowledge from a large database by using software and tools to look for discrimination and expressive patterns. This process helps companies to focus on important information in their historical databases to make decisions. Association rule mining is one of the most important domain in data mining. It...

The dramatic proliferation of information on the web and the tremendous growth in the number of files published and uploaded online each day have led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play a central role in retrieving the desired information, it becomes necessary...

This paper addresses the problem of refining large skylines by introducing a new dominance relationship. Skyline queries are powerful tool to capture user preferences. However in some contexts, the skyline is too large to give any useful insight to the user. In order to solve this problem, we introduce a strong dominance relationship that relies on...

The amount of information in a data warehouse tends to be extremely large and queries may involve several complex join and aggregate operations at the same time. By using the right indices, the database administrator can speed up these OLAP queries and dramatically shorten processing times. However, selection of an optimal set of indices is a very...

Owing to the increasing use of ambiguous and imprecise words in expressing the user’s information need, it has become necessary to expand the original query with additional terms that best capture the actual user intent. Selecting the appropriate words to be used as additional terms is mainly dependent on the degree of relatedness between a candida...

Clustering is an essential data mining tool for analyzing big data. In this article, an overview of literature methods is undertaken. Following this study, a new algorithm called BSO-CLARA is proposed for clustering large data sets. It is based on bee behavior and k-medoids partitioning. Criteria like effectiveness, eficiency, scalability and contr...

The massive growth of information and the exponential increase in the number of documents published and uploaded online each day have led to led to the appearance of new words in the Internet. Due to the difficulty of reaching the meanings of these new terms, which play a central role in retrieving the desired information, it becomes necessary to g...

This paper deals with association rules mining using evolutionary algorithms. All previous bio-inspired based association rules mining approaches generate non admissible rules, which cannot be exploited by the end-user. To cope with this issue, we propose two approaches that avoid non admissible rules by developing a new strategy called delete and...

The tremendous size of data in nowadays world web invokes many data mining techniques. The recent emergence of some new data mining techniques provide also many interesting induction rules. So, it's important to process these induction rules in order to extract some new strong patterns called meta-rules. This work explores this concept by proposing...

Unlike the previous works where detecting communities is performed on large graphs, our approach considers textual documents for discovering potential social networks. More precisely, the aim of this paper is to extract social communities from a collection of documents and a query specifying the domain of interest that may link the group. We propos...

Big data mining is a promising technology for industrial applications such as business intelligence and marketing. In this paper, we attempt to transfer this background to a scientific domain, which is problem solving. An enormous research investment has been done over the past years for solving NP-complete problems. The exact methods yield the opt...

In this paper, we propose a bat-based algorithm (BA) for association rule mining (ARM Bat). Our algorithm aims to maximize the fitness function to generate the best rules in the defined dataset starting from a specific minimum support and minimum confidence. The efficiency of our proposed algorithm is tested on several generic datasets with differe...

Association rules mining has been largely studied by the data mining community. ARM aims to extract the interesting rules from any given transactional database. This problem is well known to be time consuming in general. This paper deals with association rules mining algorithms to cope with very large databases and especially for those existing on...

The efficiency of existing association rules mining algorithms afford large number of delivered rules that the user can not exploit them easily. Consequently, thinking about another mining of these generated rules becomes essential task. For this, the present paper explores metarules extraction in order to prune the irrelevant rules. It first focus...