Search results for: graph mining

Authors: Thiago M. R. Dias, Gray F. Moita, Paulo E. M. Almeida

Abstract:

Although the World Wide Web is considered the largest source of information there exists nowadays, due to its inherent dynamic characteristics, the task of finding useful and qualified information can become a very frustrating experience. This study presents a research on the information mining systems in the Web; and proposes an implementation of these systems by means of components that can be built using the technology of Web services. This implies that they can encompass features offered by a services oriented architecture (SOA) and specific components may be used by other tools, independent of platforms or programming languages. Hence, the main objective of this work is to provide an architecture to Web mining systems, divided into stages, where each step is a component that will incorporate the characteristics of SOA. The separation of these steps was designed based upon the existing literature. Interesting results were obtained and are shown here.

Keywords: Web Mining, Service Oriented Architecture, WebServices.

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Authors: Jan Rychtar, Brian Stadler

Abstract:

We study how the outcome of evolutionary dynamics on graphs depends on a randomness on the graph structure. We gradually change the underlying graph from completely regular (e.g. a square lattice) to completely random. We find that the fixation probability increases as the randomness increases; nevertheless, the increase is not significant and thus the fixation probability could be estimated by the known formulas for underlying regular graphs.

Keywords: evolutionary dynamics, small-world networks

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Authors: Verónica Díaz

Abstract:

A Cartesian graph, as a mathematical object, becomes a tool for configuration of change. Its best comprehension is done through everyday life problem-solving associated with its representation. Despite this, the current educational framework favors general graphs, without consideration of their argumentation. Students are required to find the mathematical function without associating it to the development of graphical language. This research describes the use made by students of configurations made prior to Cartesian graphs with regards to an everyday life problem related to a time and distance variation phenomenon. The theoretical framework describes the function conditions of study and their modeling. This is a qualitative, descriptive study involving six undergraduate case studies that were carried out during the first term in 2016 at University of Los Lagos. The research problem concerned the graphic modeling of a real person’s movement phenomenon, and two levels of analysis were identified. The first level aims to identify local and global graph interpretations; a second level describes the iconicity and referentiality degree of an image. According to the results, students were able to draw no figures before the Cartesian graph, highlighting the need for students to represent the context and the movement of which causes the phenomenon change. From this, they managed Cartesian graphs representing changes in position, therefore, achieved an overall view of the graph. However, the local view only indicates specific events in the problem situation, using graphic and verbal expressions to represent movement. This view does not enable us to identify what happens on the graph when the movement characteristics change based on possible paths in the person’s walking speed.

Keywords: Cartesian graphs, higher education, movement modeling, problem solving.

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Authors: Elias O. Tembe, Hussain A. Al-Salamin

Abstract:

There is decagram of strategic decisions of operations and production/service management (POSM) within operational research (OR) which must collate, namely: design, inventory, quality, location, process and capacity, layout, scheduling, maintain ace, and supply chain. This paper presents an architectural configuration conceptual framework of a decagram of sets decisions in a form of mathematical complete graph and abelian graph. Mathematically, a complete graph is undirected (UDG), and directed (DG) a relationship where every pair of vertices is connected, collated, confluent, and holomorphic. There has not been any study conducted which, however, prioritizes the holomorphic sets which of POMS within OR field of study. The study utilizes OR structured technique known as The Analytic Hierarchy Process (AHP) analysis for organizing, sorting and prioritizing(ranking) the sets within the decagram of POMS according to their attribution (propensity), and provides an analysis how the prioritization has real-world application within the 21st century.

Keywords: AHP analysis, Decagram, Decagon, Holomorphic.

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Authors: Alicia Y. C. Tang, Rukaini Abdullah, Sharifuddin M. Zain, Noorsaadah A. Rahman

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This paper discusses the causal explanation capability of QRIOM, a tool aimed at supporting learning of organic chemistry reactions. The development of the tool is based on the hybrid use of Qualitative Reasoning (QR) technique and Qualitative Process Theory (QPT) ontology. Our simulation combines symbolic, qualitative description of relations with quantity analysis to generate causal graphs. The pedagogy embedded in the simulator is to both simulate and explain organic reactions. Qualitative reasoning through a causal chain will be presented to explain the overall changes made on the substrate; from initial substrate until the production of final outputs. Several uses of the QPT modeling constructs in supporting behavioral and causal explanation during run-time will also be demonstrated. Explaining organic reactions through causal graph trace can help improve the reasoning ability of learners in that their conceptual understanding of the subject is nurtured.

Keywords: Qualitative reasoning, causal graph, organicreactions, explanation, QPT, modeling constructs.

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Authors: M. Anandhavalli Gauthaman

Abstract:

DNA microarrays allow the measurement of expression levels for a large number of genes, perhaps all genes of an organism, within a number of different experimental samples. It is very much important to extract biologically meaningful information from this huge amount of expression data to know the current state of the cell because most cellular processes are regulated by changes in gene expression. Association rule mining techniques are helpful to find association relationship between genes. Numerous association rule mining algorithms have been developed to analyze and associate this huge amount of gene expression data. This paper focuses on some of the popular association rule mining algorithms developed to analyze gene expression data.

Keywords: DNA microarray, gene expression, association rule mining.

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Authors: Mourad Ykhlef, Hebah ElGibreen

Abstract:

Mining Sequential Patterns in large databases has become an important data mining task with broad applications. It is an important task in data mining field, which describes potential sequenced relationships among items in a database. There are many different algorithms introduced for this task. Conventional algorithms can find the exact optimal Sequential Pattern rule but it takes a long time, particularly when they are applied on large databases. Nowadays, some evolutionary algorithms, such as Particle Swarm Optimization and Genetic Algorithm, were proposed and have been applied to solve this problem. This paper will introduce a new kind of hybrid evolutionary algorithm that combines Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) to mine Sequential Pattern, in order to improve the speed of evolutionary algorithms convergence. This algorithm is referred to as SP-GAPSO.

Keywords: Genetic Algorithm, Hybrid Evolutionary Algorithm, Particle Swarm Optimization algorithm, Sequential Pattern mining.

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Authors: S. Sadallah

Abstract:

In this paper, dynamic programming is used to determine the optimal management of financial resources in company. Solution of the problem by consider into simpler substructures is constructed. The optimal management of internal capital of company are simulated. The tools applied in this development are based on graph theory. The software of given problems is built by using greedy algorithm. The obtained model and program maintenance enable us to define the optimal version of management of proper financial flows by using visual diagram on each level of investment.

Keywords: Management, software, optimal, greedy algorithm, graph-diagram.

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Authors: Mohammad Shoyaib, M. Abdullah-Al-Wadud, Oksam Chae

Abstract:

Proteomics is one of the largest areas of research for bioinformatics and medical science. An ambitious goal of proteomics is to elucidate the structure, interactions and functions of all proteins within cells and organisms. Predicting Protein-Protein Interaction (PPI) is one of the crucial and decisive problems in current research. Genomic data offer a great opportunity and at the same time a lot of challenges for the identification of these interactions. Many methods have already been proposed in this regard. In case of in-silico identification, most of the methods require both positive and negative examples of protein interaction and the perfection of these examples are very much crucial for the final prediction accuracy. Positive examples are relatively easy to obtain from well known databases. But the generation of negative examples is not a trivial task. Current PPI identification methods generate negative examples based on some assumptions, which are likely to affect their prediction accuracy. Hence, if more reliable negative examples are used, the PPI prediction methods may achieve even more accuracy. Focusing on this issue, a graph based negative example generation method is proposed, which is simple and more accurate than the existing approaches. An interaction graph of the protein sequences is created. The basic assumption is that the longer the shortest path between two protein-sequences in the interaction graph, the less is the possibility of their interaction. A well established PPI detection algorithm is employed with our negative examples and in most cases it increases the accuracy more than 10% in comparison with the negative pair selection method in that paper.

Keywords: Interaction graph, Negative training data, Protein-Protein interaction, Support vector machine.

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Authors: Babak Abedin, Babak Sohrabi

Abstract:

Website plays a significant role in success of an e-business. It is the main start point of any organization and corporation for its customers, so it's important to customize and design it according to the visitors' preferences. Also, websites are a place to introduce services of an organization and highlight new service to the visitors and audiences. In this paper, we will use web usage mining techniques, as a new field of research in data mining and knowledge discovery, in an Iranian government website. Using the results, a framework for web content layour is proposed. An agent is designed to dynamically update and improve web links locations and layout. Then, we will explain how it is used to directly enable top managers of the organization to influence on the arrangement of web contents and also to enhance customization of web site navigation due to online users' behaviors.

Keywords: Web usage mining, website design, agent, website customization.

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Authors: Reza Meimandi Parizi, Abdul Azim Abdul Ghani

Abstract:

The ever-growing usage of aspect-oriented development methodology in the field of software engineering requires tool support for both research environments and industry. So far, tool support for many activities in aspect-oriented software development has been proposed, to automate and facilitate their development. For instance, the AJaTS provides a transformation system to support aspect-oriented development and refactoring. In particular, it is well established that the abstract interpretation of programs, in any paradigm, pursued in static analysis is best served by a high-level programs representation, such as Control Flow Graph (CFG). This is why such analysis can more easily locate common programmatic idioms for which helpful transformation are already known as well as, association between the input program and intermediate representation can be more closely maintained. However, although the current researches define the good concepts and foundations, to some extent, for control flow analysis of aspectoriented programs but they do not provide a concrete tool that can solely construct the CFG of these programs. Furthermore, most of these works focus on addressing the other issues regarding Aspect- Oriented Software Development (AOSD) such as testing or data flow analysis rather than CFG itself. Therefore, this study is dedicated to build an aspect-oriented control flow graph construction tool called AJcFgraph Builder. The given tool can be applied in many software engineering tasks in the context of AOSD such as, software testing, software metrics, and so forth.

Keywords: Aspect-Oriented Software Development, AspectJ, Control Flow Graph, Data Flow Analysis

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Authors: Nabil M. Hewahi, Motaz K. Saad

Abstract:

In large datasets, identifying exceptional or rare cases with respect to a group of similar cases is considered very significant problem. The traditional problem (Outlier Mining) is to find exception or rare cases in a dataset irrespective of the class label of these cases, they are considered rare events with respect to the whole dataset. In this research, we pose the problem that is Class Outliers Mining and a method to find out those outliers. The general definition of this problem is “given a set of observations with class labels, find those that arouse suspicions, taking into account the class labels". We introduce a novel definition of Outlier that is Class Outlier, and propose the Class Outlier Factor (COF) which measures the degree of being a Class Outlier for a data object. Our work includes a proposal of a new algorithm towards mining of the Class Outliers, presenting experimental results applied on various domains of real world datasets and finally a comparison study with other related methods is performed.

Keywords: Class Outliers, Distance-Based Approach, Outliers Mining.

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Authors: Marcin Michalak

Abstract:

Due to the increasing efforts on saving our natural environment a change in the structure of energy resources can be observed - an increasing fraction of a renewable energy sources. In many countries traditional underground coal mining loses its significance but there are still countries, like Poland or Germany, in which the coal based technologies have the greatest fraction in a total energy production. This necessitates to make an effort to limit the costs and negative effects of underground coal mining. The longwall complex is as essential part of the underground coal mining. The safety and the effectiveness of the work is strongly dependent of the diagnostic state of powered roof supports. The building of a useful and reliable diagnostic system requires a lot of data. As the acquisition of a data of any possible operating conditions it is important to have a possibility to generate a demanded artificial working characteristics. In this paper a new approach of modelling a leg pressure in the single unit of powered roof support. The model is a result of the analysis of a typical working cycles.

Keywords: Machine modelling, underground mining, coal mining.

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Authors: S. Soommat, S. Patamatamkul, T. Prempridi, M. Sritulyachot, P. Ineure, S. Yimman

Abstract:

Currently, slider process of Hard Disk Drive Industry become more complex, defective diagnosis for yield improvement becomes more complicated and time-consumed. Manufacturing data analysis with data mining approach is widely used for solving that problem. The existing mining approach from combining of the KMean clustering, the machine oriented Kruskal-Wallis test and the multivariate chart were applied for defective diagnosis but it is still be a semiautomatic diagnosis system. This article aims to modify an algorithm to support an automatic decision for the existing approach. Based on the research framework, the new approach can do an automatic diagnosis and help engineer to find out the defective factors faster than the existing approach about 50%.

Keywords: Slider process, Defective diagnosis and Data mining.

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Authors: Sahil, Rajeev Saha, Sanjeev Kumar

Abstract:

Material selection is one of the key issues for the production of reliable and quality products in industries. A number of materials are available for a single product due to which material selection become a difficult task. The aim of this paper is to select appropriate material for gear used in fuel pump by using Graph Theory and Matrix Approach (GTMA). GTMA is a logical and systematic approach that can be used to model and analyze various engineering systems. In present work, four alternative material and their seven attributes are used to identify the best material for given product.

Keywords: Material, GTMA, MADM, digraph, decision making.

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Authors: Lilac A. E. Al-Safadi

Abstract:

This paper proposes an auto-classification algorithm of Web pages using Data mining techniques. We consider the problem of discovering association rules between terms in a set of Web pages belonging to a category in a search engine database, and present an auto-classification algorithm for solving this problem that are fundamentally based on Apriori algorithm. The proposed technique has two phases. The first phase is a training phase where human experts determines the categories of different Web pages, and the supervised Data mining algorithm will combine these categories with appropriate weighted index terms according to the highest supported rules among the most frequent words. The second phase is the categorization phase where a web crawler will crawl through the World Wide Web to build a database categorized according to the result of the data mining approach. This database contains URLs and their categories.

Keywords: Information Processing on the Web, Data Mining, Document Classification.

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Authors: Yi Chao Huang, Yu Ling Liao, Chiu Shuang Lin

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A data cutting and sorting method (DCSM) is proposed to optimize the performance of data mining. DCSM reduces the calculation time by getting rid of redundant data during the data mining process. In addition, DCSM minimizes the computational units by splitting the database and by sorting data with support counts. In the process of searching for the relationship between metabolic syndrome and lifestyles with the health examination database of an electronics manufacturing company, DCSM demonstrates higher search efficiency than the traditional Apriori algorithm in tests with different support counts.

Keywords: Data mining, Data cutting and sorting method, Apriori algorithm, Metabolic syndrome

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Authors: Xun Ge, Jianhua Gong

Abstract:

 

ankings for output of Chinese main agricultural commodity in the world for 1978, 1980, 1990, 2000, 2006, 2007 and 2008 have been released in United Nations FAO Database. Unfortunately, where the ranking of output of Chinese cotton lint in the world for 2008 was missed. This paper uses sequential data mining methods with decision rules filling this gap. This new data mining method will be help to give a further improvement for United Nations FAO Database.

Keywords: Ranking, output of the main agricultural commodity, gross domestic product, decision table, information system, data mining, decision rule

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Authors: Mahdi Esmaeili, Mansour Tarafdar

Abstract:

The goal of data mining algorithms is to discover useful information embedded in large databases. One of the most important data mining problems is discovery of frequently occurring patterns in sequential data. In a multidimensional sequence each event depends on more than one dimension. The search space is quite large and the serial algorithms are not scalable for very large datasets. To address this, it is necessary to study scalable parallel implementations of sequence mining algorithms. In this paper, we present a model for multidimensional sequence and describe a parallel algorithm based on data parallelism. Simulation experiments show good load balancing and scalable and acceptable speedup over different processors and problem sizes and demonstrate that our approach can works efficiently in a real parallel computing environment.

Keywords: Sequential Patterns, Data Mining, ParallelAlgorithm, Multidimensional Sequence Data

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Authors: Joanna M. Dulińska, Maria Fabijańska

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In the paper the results of calculations of the dynamic response of a multi-storey reinforced concrete building to a strong mining shock originated from the main region of mining activity in Poland (i.e. the Legnica-Glogow Copper District) are presented. The representative time histories of accelerations registered in three directions were used as ground motion data in calculations of the dynamic response of the structure. Two variants of a numerical model were applied: the model including only structural elements of the building and the model including both structural and non-structural elements (i.e. partition walls and ventilation ducts made of brick). It turned out that non-structural elements of multi-storey RC buildings have a small impact of about 10 % on natural frequencies of these structures. It was also proved that the dynamic response of building to mining shock obtained in case of inclusion of all non-structural elements in the numerical model is about 20 % smaller than in case of consideration of structural elements only. The principal stresses obtained in calculations of dynamic response of multi-storey building to strong mining shock are situated on the level of about 30% of values obtained from static analysis (dead load).

Keywords: Dynamic characteristics of buildings, mining shocks, dynamic response of buildings, non-structural elements

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Authors: M. Kargar, R. Mirmiran, F. Fartash, T. Saderi

Abstract:

In this paper a new method is suggested for risk management by the numerical patterns in data-mining. These patterns are designed using probability rules in decision trees and are cared to be valid, novel, useful and understandable. Considering a set of functions, the system reaches to a good pattern or better objectives. The patterns are analyzed through the produced matrices and some results are pointed out. By using the suggested method the direction of the functionality route in the systems can be controlled and best planning for special objectives be done.

Keywords: Analysis, Data-mining, Pattern, Risk Management.

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Authors: Faisal Aburub, Wael Hadi

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Data mining is the process of extracting useful or hidden information from a large database. Extracted information can be used to discover relationships among features, where data objects are grouped according to logical relationships; or to predict unseen objects to one of the predefined groups. In this paper, we aim to investigate four well-known data mining algorithms in order to predict groundwater areas in Jordan. These algorithms are Support Vector Machines (SVMs), Naïve Bayes (NB), K-Nearest Neighbor (kNN) and Classification Based on Association Rule (CBA). The experimental results indicate that the SVMs algorithm outperformed other algorithms in terms of classification accuracy, precision and F1 evaluation measures using the datasets of groundwater areas that were collected from Jordanian Ministry of Water and Irrigation.

Keywords: Classification, data mining, evaluation measures, groundwater.

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Authors: Seulbi Choi, Hyunchul Ahn

Abstract:

Collaborative filtering (CF) algorithm has been popularly used for recommender systems in both academic and practical applications. It basically generates recommendation results using users’ numeric ratings. However, the additional use of the information other than user ratings may lead to better accuracy of CF. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's review can be regarded as the new informative source for identifying user's preference with accuracy. Under this background, this study presents a hybrid recommender system that fuses CF and user's review mining. Our system adopts conventional memory-based CF, but it is designed to use both user’s numeric ratings and his/her text reviews on the items when calculating similarities between users.

Keywords: Recommender system, collaborative filtering, text mining, review mining.

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Authors: Bassam Atieh Rajabi, Sai Peck Lee

Abstract:

Business Process Modeling (BPM) is the first and most important step in business process management lifecycle. Graph based formalism and rule based formalism are the two most predominant formalisms on which process modeling languages are developed. BPM technology continues to face challenges in coping with dynamic business environments where requirements and goals are constantly changing at the execution time. Graph based formalisms incur problems to react to dynamic changes in Business Process (BP) at the runtime instances. In this research, an adaptive and flexible framework based on the integration between Object Oriented diagramming technique and Petri Net modeling language is proposed in order to support change management techniques for BPM and increase the representation capability for Object Oriented modeling for the dynamic changes in the runtime instances. The proposed framework is applied in a higher education environment to achieve flexible, updatable and dynamic BP.

Keywords: Business Process Modeling, Change Management, Graph Based Modeling, Rule Based Modeling, Object Oriented PetriNet.

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Authors: K. Ratan Singh, P. K. Das

Abstract:

An induced acyclic graphoidal cover of a graph G is a collection ψ of open paths in G such that every path in ψ has atleast two vertices, every vertex of G is an internal vertex of at most one path in ψ, every edge of G is in exactly one path in ψ and every member of ψ is an induced path. The minimum cardinality of an induced acyclic graphoidal cover of G is called the induced acyclic graphoidal covering number of G and is denoted by ηia(G) or ηia. Here we find induced acyclic graphoidal cover for some classes of graphs.

Keywords: Graphoidal cover, Induced acyclic graphoidal cover, Induced acyclic graphoidal covering number.

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Authors: Karima Qayumi, Alex Norta

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The rapid generation of high volume and a broad variety of data from the application of new technologies pose challenges for the generation of business-intelligence. Most organizations and business owners need to extract data from multiple sources and apply analytical methods for the purposes of developing their business. Therefore, the recently decentralized data management environment is relying on a distributed computing paradigm. While data are stored in highly distributed systems, the implementation of distributed data-mining techniques is a challenge. The aim of this technique is to gather knowledge from every domain and all the datasets stemming from distributed resources. As agent technologies offer significant contributions for managing the complexity of distributed systems, we consider this for next-generation data-mining processes. To demonstrate agent-based business intelligence operations, we use agent-oriented modeling techniques to develop a new artifact for mining massive datasets.

Keywords: Agent-oriented modeling, business Intelligence management, distributed data mining, multi-agent system.

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Authors: Hany Mahgoub, Dietmar Rösner, Nabil Ismail, Fawzy Torkey

Abstract:

This paper describes text mining technique for automatically extracting association rules from collections of textual documents. The technique called, Extracting Association Rules from Text (EART). It depends on keyword features for discover association rules amongst keywords labeling the documents. In this work, the EART system ignores the order in which the words occur, but instead focusing on the words and their statistical distributions in documents. The main contributions of the technique are that it integrates XML technology with Information Retrieval scheme (TFIDF) (for keyword/feature selection that automatically selects the most discriminative keywords for use in association rules generation) and use Data Mining technique for association rules discovery. It consists of three phases: Text Preprocessing phase (transformation, filtration, stemming and indexing of the documents), Association Rule Mining (ARM) phase (applying our designed algorithm for Generating Association Rules based on Weighting scheme GARW) and Visualization phase (visualization of results). Experiments applied on WebPages news documents related to the outbreak of the bird flu disease. The extracted association rules contain important features and describe the informative news included in the documents collection. The performance of the EART system compared with another system that uses the Apriori algorithm throughout the execution time and evaluating extracted association rules.

Keywords: Text mining, data mining, association rule mining

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Authors: Somayeh Alizadeh, Mehdi Ghazanfari, Mohammad Fathian

Abstract:

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show relations between essential components. In some FCM, there are more nodes, which related to each other and more nodes means more complex in system behaviors and analysis. In this paper, a novel learning method used to construct FCMs based on historical data and by using data mining and DEMATEL method, a new method defined to reduce nodes number. This method cluster nodes in FCM based on their cause and effect behaviors.

Keywords: Clustering, Data Mining, Fuzzy Cognitive Map(FCM), Learning.

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Authors: Scott N. Gerard, Aliza Heching, Susann M. Keohane, Samuel S. Adams

Abstract:

The world-wide population of people over 60 years of age is growing rapidly. The explosion is placing increasingly onerous demands on individual families, multiple industries and entire countries. Current, human-intensive approaches to eldercare are not sustainable, but IoT and AI technologies can help. The Knowledge Reactor (KR) is a contextual, data fusion engine built to address this and other similar problems. It fuses and centralizes IoT and System of Record/Engagement data into a reactive knowledge graph. Cognitive applications and services are constructed with its multiagent architecture. The KR can scale-up and scaledown, because it exploits container-based, horizontally scalable services for graph store (JanusGraph) and pub-sub (Kafka) technologies. While the KR can be applied to many domains that require IoT and AI technologies, this paper describes how the KR specifically supports the challenging domain of cognitive eldercare. Rule- and machine learning-based analytics infer activities of daily living from IoT sensor readings. KR scalability, adaptability, flexibility and usability are demonstrated.

Keywords: Ambient sensing, AI, artificial intelligence, eldercare, IoT, internet of things, knowledge graph.

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Authors: Mahsa Tavakoli, Seyed Mohammad Hojjati, Yahya Kooch

Abstract:

Considering that coal mining is one of the important industrial activities, it may cause damages to environment. According to the author’s best knowledge, the effect of traditional coal mining activities on plant biodiversity has not been investigated in the Hyrcanian forests. Therefore, in this study, the effect of coal mining activities on vegetation and tree diversity was investigated in Hyrcanian forest, North Iran. After filed visiting and determining the mine, 16 plots (20×20 m²) were established by systematic-randomly (60×60 m²) in an area of 4 ha (200×200 m²-mine entrance placed at center). An area adjacent to the mine was not affected by the mining activity, and it is considered as the control area. In each plot, the data about trees such as number and type of species were recorded. The biodiversity of vegetation cover was considered 5 square sub-plots (1 m²) in each plot. PAST software and Ecological Methodology were used to calculate Biodiversity indices. The value of Shannon Wiener and Simpson diversity indices for tree cover in control area (1.04±0.34 and 0.62±0.20) was significantly higher than mining area (0.78±0.27 and 0.45±0.14). The value of evenness indices for tree cover in the mining area was significantly lower than that of the control area. The value of Shannon Wiener and Simpson diversity indices for vegetation cover in the control area (1.37±0.06 and 0.69±0.02) was significantly higher than the mining area (1.02±0.13 and 0.50±0.07). The value of evenness index in the control area was significantly higher than the mining area. Plant communities are a good indicator of the changes in the site. Study about changes in vegetation biodiversity and plant dynamics in the degraded land can provide necessary information for forest management and reforestation of these areas.

Keywords: Vegetation biodiversity, species composition, traditional coal mining, caspian forest.

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