Formal Concept Analysis (Data Mining)

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented ...

This study aimed at developing a system using support vector machine (SVM) that will forecast sales of farm products for an agricultural farm so that managers can take strategic decisions timely to better market the excess farm products which some by nature are perishable. The sales prediction model used SVMs and Fuzzy Theory. The implementation was done using Python Programming Language. The system comprised of three (3) modules: web interface, flask and the SVM Framework. To evaluate the result of the SVM model, the RBF neural network was used as a benchmark. Data of previous sales records from University of Agriculture Makurdi (UAM) farm was used to train and test the system. After training the network with data which covered the time period

The emergence of big data analytics as a way of deriving insights from data brought excitement to mathematicians, statisticians, computer scientists and other professionals. However, the absence of a mathematical foundation for analytics has become a real challenge amidst the flock of big data marketing activities, especially in healthcare insurance. This paper developed a mathematical model for the analytics of healthcare insurance data using set theory. We implemented a prototype of the model using Java Programming Language, MapReduce Framework, Association Rule Mining and MongoDB. Also, we tested the model accuracy using data from the National Health Insurance Scheme in Nigeria to reduce delay. The result showed that the accuracy level was 97.14% on average, which depicts a higher performance for the model. This result implies that delays affecting the processing of data submitted by the providers and enrollees to the HMOs reduced drastically leading to the improvement in the eas...

This paper introduces current acoustic theories relating to the phenomenology of sound as a framework for interrogating concepts relating to the ecologies of acoustic and landscape phenomena in a Japanese stroll garden. By applying the technique of Formal Concept Analysis, a partially ordered lattice of garden objects and attributes is visualized as a means to investigate the relationship between elements of the taxonomy.

Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.

With the explosive growth in the world's population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemen...

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data

In Formal Concept Analysis (FCA), a concept lattice graphically portrays the underlying relationships between the objects and attributes of an information system. One of the key complexity problems of concept lattices lies in extracting the useful information. The unorganized nature of attributes in huge contexts often does not yield an informative lattice in FCA. Moreover, understanding the collective relationships between attributes and objects in a larger many valued context is more complicated. In this paper, we introduce a novel approach for deducing a smaller and meaningful concept lattice from which excerpts of concepts can be inferred. In existing attribute-based concept lattice reduction methods for FCA, mostly either the attribute size or the context size is reduced. Our approach involves in organizing the attributes into clusters using their structural similarities and dissimilarities, which is commonly known as attribute clustering, to produce a derived context. We have observed that the deduced concept lattice inherits the structural relationship of the original one. Furthermore, we have mathematically proved that a unique surjective inclusion mapping from the original lattice to the deduced one exists.

In the present scenario, a person wants ease in their lives, so E-commerce has become a great and admirable involvement in providing the availability of any product at the doorsteps. But how a person can know the efficiency and originality of the product just by looking at the pictures and the details of the product on the websites. To overcome these issues the E-commerce websites have introduced the concept of the Reviews. Reviews are written by the customers who have already purchased it. Studies show that Product reviews are one of the most important points one considers during the purchasing from E-commerce websites like Flipkart, Snapdeal, Amazon and so on. This paper proposes a model that detects whether the given review is positive, negative, or neutral using the method of sentiment analysis. And using Data Analysis we can find the extension of this paper, we are planning to use a type of sentiment analysis, Opinion Mining which is the research field that predominantly makes automatic systems that will find opinion from the text written in human language. Using opinion mining, we can find whether the given reviews are fake or not. In this paper we have used Amazon food reviews data and based on the rating given by the user we are classifying reviews as positive, negative, or neutral. For positive review ratings given were 4 and 5. For negative review ratings given were 1 and 2. For neutral, rating given was 3. Based on these ratings, we are performing sentiment analysis using Scikit Learn and finding the accuracies of various classification algorithms. We are using Jupyter Notebook for visualization of documents and live coding.

A very important issue with the e-commerce delivery service in most of the emerging economies including India is the last mile connectivity. Delivering products, booked online to the remote tier-2 and tier-3 cities remained “costly”. It is observed from firsthand experience with some well-known e-commerce brands in India that their delivery service partners tend to cancel orders that are far away from their tier-2 logistics hubs with the reason shown as “address out of delivery range”. Due to low order density in the far flanges of tier-2 and tier-3 cities arranging vehicles and delivery personnel become costly. In this paper, we propose an innovative delivery model to serve the remote areas by opening edge-hubs at selected places and employing local daily commuters for last mile delivery. Identifying the edge-hubs for opening distribution centers is a costly business if done using traditional field surveys. Here we propose the use of telecom call detail record (CDR) location data a...

Stress will exist once operating or paying attention to somebody shouts at you. It is caused through conflicts, creating selections, or otherwise straining skills, or it will exist owing to various social demands on time. it's noted that emotional states tends to not be long lasting, whether or not they are unpleasant emotions like worry, or pleasant ones, like joy. Nonetheless infrequently such states might persist for long periods of your time, or they will reach too high levels. Once this happens, the result typically is labeled "stress", and it's psychological as well as physiological terms. Stress and its manifestations, such as anxiety, depression, and burnout, have always have been a common problem among people in different professions and occupations. In the last few decades, alarm has already been provoked by the proliferation of books, research reports, popular articles and the growing number of organized workshops, aiming to teach people how to cope up with it. The purpose of the study is to find out the level of academic stress among higher secondary students. The present study consists of students studying in higher secondary schools situated in Tamil Nadu, India. The sample was selected by using simple random sampling technique. The present study reveals that the higher secondary students are having moderate level of academic stress and irrespective of sub samples of the higher secondary students are having moderate level of academic stress. The male student's academic stress is higher than female students. The urban student's tutorial stress is higher than rural students. The government college student's tutorial stress is a smaller amount than school students. The science subject student's tutorial stress is over arts student. The scholars whose parents are literates, the educational stress is over their counterparts.

Recently Formal Concept Analysis (FCA), a mathematical framework based on partial ordering relations has become popular for knowledge representation and reasoning. Further this framework is extended as Fuzzy FCA, Rough FCA, etc. to deal with practical applications. There are investigations in the literature applying FCA for Information Retrieval (IR) applications. The objective of this paper is to apply Fuzzy FCA approach for IR. While adopting Fuzzy FCA, we follow a fast algorithm to generate the fuzzy concepts rather than classical algorithms that are based on residuated methods. Further we follow an approach that retrieves the relevant documents even during absence of exact match of the keywords.

9th International Conference on Data Mining and Applications (DMAP
2022) provides  a  forum  for  researchers  who  address  this  issue  and  to  present their  work
in  a  peer-reviewed  forum.  Authors  are  solicited  to  contribute  to  the  conference  by
submitting articles that illustrate research results, projects, surveying works and industrial
experiences that describe significant advances in Data mining and Applications.
Authors are solicited to contribute to the conference by  submitting articles that illustrate
research results, projects, surveying works and industrial experiences that describe
significant advances in the following areas but are not limited to:

In this paper is analyzed the design of a software platform concerning a case study of process engineering involving the simultaneous adoption of data digitation, Data Mining-DM-processing, and Augmented Reality-AR-. Specifically is discussed the platform design able to upgrade the Knowledge Base-KB-enabling production process optimizations in working sites. The KB is gained by following 'Frascati' research guidelines addressing the possible ways to achieve the Knowledge Gain-KG-. The technologies such as AR and data entry mobile app are tailored in order to apply innovative data mining algorithms. In the first part of the paper is commented the preliminary project specifications, besides, in the second part, are shown the use cases, the unified modeling language-UML-models, and the mobile app mockups enabling KG. The proposed work discusses preliminary results of an industry project.

There is different decision tree based algorithms in data mining tools. These algorithms are used for classification of data objects and used for decision making purpose. This study determines the decision tree based ID3 algorithm and its implementation with student data example.

Fuzzy Formal Concept Analysis (FCA) is a mathematical tool for the effective representation of imprecise and vague knowledge. However, with a large number of formal concepts from a fuzzy context, the task of knowledge representation becomes complex. Hence, knowledge reduction is an important issue in FCA with a fuzzy setting. The purpose of this current study is to address this issue by proposing a method that computes the corresponding crisp order for the fuzzy relation in a given fuzzy formal context. The obtained formal context using the proposed method provides a fewer number of concepts when compared to original fuzzy context. The resultant lattice structure is a reduced form of its corresponding fuzzy concept lattice and preserves the specialized and generalized concepts, as well as stability. This study also shows a step-by-step demonstration of the proposed method and its application.

Accounting databases with fraudulent transactions inside was used to detect fraud patterns by data mining tool. The object was accomplished by the following method: first, inside data, fraudulent transactions according to three fraud patterns were settled, over it the algorithms, Euclidian distance and local outlier factors were run using Rapidminer program. As a result the fraud patterns were shown in different ways according to the specific graphics contributed by the program. In conclusion, clusters grouping by Euclidian distance with k Means algorithm (k = 4) allowed an adequate visualization of the values’ distribution, as consequence was detected the first and third fraud patterns. The application of the outlier’s detection algorithm (LOF) detected the three fraud patterns in a clear way as a consequence of the insolate outliers in different graphics, shown by Rapidminer program, with different variables correlated.

There are several tools that use techniques like static analysis, lexical analysis etc to detect the vulnerabilities in Java based programs. However still there are vulnerabilities which are not traceable by the available tools. The objective of this
paper is twofold. We develop a method to detect vulnerabilities in Java programs. Further we analyze the dependencies among the vulnerabilities using mathematical lattice theory based formal concept analysis. Our experimental result show that the
proposed model is able to detect the untraceable vulnerabilities and the dependency analysis is in good agreement with the literature.

Edgar Allan Poe wrote seventy short stories in his
lifetime, and literary critics have categorized these stories
in many ways, e.g., by genres such as horror, detective or
proto-science fiction. This paper discusses how a
computer can group stories by using families of words
related by a theme, e.g., words denoting colors. This
approach combines two different techniques. First, we use
term-document matrices, which were originally developed
for document searches in the field of information
retrieval. Second, we use formal concept theory, which
defines concepts in a way that forms a Galois lattice.
These lattices have both a well developed mathematical
basis and have been used in applications beyond the
computer sciences, e.g., social networks in mathematical
sociology. Finally, we discuss how meaningful these
groups of stories are to a human reader.

In this article we introduce the semiotic square by A. J. Greimas and the notions of negation and opposition that were central to the Paris school of structural semiotics. We trace the connection of the square to both Aristotle’s square of opposition and the Klein four-group as well as propose a formalization of the square. This is firstly achieved through identifying R-relations on meta-term/seme pairs of the square, then applying lattice theory and formal concept analysis in order to visualize an extended structure. Our main result is a protoconcept algebra that generalizes the Greimas square through Boolean operations, and provides an ordering of all possible formal concepts, thus acting as a taxonomy.

Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data.

Data mining and knowledge discovery of database is magnetizing wide array of non-trivial research arena, making easy to industrial decision support systems and continues to expand even beyond imagination in one such promising field like Artificial Intelligence and facing the real world challenges. Association rules  forms an important paradigm in the field of data mining for various databases like transactional database, time-series database, spatial, object-oriented databases etc. The burgeoning amount of data in multiple heterogeneous sources coalesces with the impediment in building and preserving central vital repositories compels the need for effectual distributive mining techniques. The majority of the previous studies rely on an Apriori-like candidate set generation-and-test approach. For these applications, these forms of aged techniques are found to be quite expensive, sluggish and highly subjective in case there exists long length patterns.

Abstract - Digital medical image plays an important role in the field of Telemedicine, but patient privacy and other security issues are still coming under threats from unauthorized users. Watermarking has become essential and required for proving the identity of the owner, as well as authorizing use of images and also protecting copyrighted material from illegal access. Authorizing use and copyright protection as parameters of an image, especially of a medical image, are strongly related to robustness and complexity, imperceptibility and capacity as parameters of watermarking. Medical images have a special structure which comprises header and body blocks. The region of non interest (NROI) in the body of a medical image is the most suitable area for embedding watermark to prevent the degradation of the medical image. In our paper, we propose a novel watermarking approach in the spatial domain, based on formal concept analysis (FCA). FCA finds the optimal position for watermark embedding in NROI of the medical image. Our watermark is built from some existing information in the DICOM header (IODs). Experimental results indicate that the proposed approach would offer us high robustness with less complexity, imperceptible embedding and low payload compared with the existing watermarking approaches.

Keywords - FCA; Watermarking; Spatial Domain; Attacks; Robustness.

Data Mining is an analytic process designed to find out data in search of harmonious patterns and methodical relationships between variables, and then to validate the extractive by applying the detected patterns to new subsets of data. The data mining is defined as the procedure of extracting information from enormous sets of data. In other words, we can say that data mining is mining knowledge from data. Afore, the scope of data mining has thoroughly been reviewed and surveyed by many researchers pertaining to the domain of healthcare industry which is an active interdisciplinary area of research. Actually, the task of knowledge extraction from the healthcare industry in medical data is a challenging effort and it is a very complex task. The present scenario in healthcare industry heart illness is a term that assigns to a huge number of health care circumstances related to heart. These medical situations relate to the unexpected health situation that straight control the cardiac. I...

In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying the formal concepts and constructing the concept lattice (digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in large datasets, various distributed algorithms are available. However, the existing distributed algorithms are not well suited for concept generation, because the generation of concepts is an iterative process. Existing algorithms are implemented using distributed frameworks like MapReduce and Open MP. These frameworks are not appropriate for iterative applications. Hence, there is a need for efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts. In this paper, we present efficient algorithms using Apache Spark. The various performance metrics used in evaluation prove that the proposed algorithms are more efficient for concept generation and lattice graph construction than existing algorithms.

Biclustering numerical data became a popular data-mining task at
the beginning of 2000's, especially for gene expression data analysis and recom-
mender systems. A bicluster re
ects a strong association between a subset of
objects and a subset of attributes in a numerical object/attribute data-table.
So-called biclusters of similar values can be thought as maximal sub-tables with
close values. Only few methods address a complete, correct and non-redundant
enumeration of such patterns, a well-known intractable problem, while no for-
mal framework exists. We introduce important links between biclustering and
Formal Concept Analysis (FCA). Indeed, FCA is known to be, among others,
a methodology for biclustering binary data. Handling numerical data is not
direct, and we argue that Triadic Concept Analysis (TCA), the extension of
FCA to ternary relations, provides a powerful mathematical and algorithmic
framework for biclustering numerical data. We discuss hence both theoretical
and computational aspects on biclustering numerical data with triadic concept
analysis. These results also scale to n-dimensional numerical datasets.

This study explored the use of formal concept analysis (FCA), a data mining technique, to analyze coral reef transect data (in terms of life forms) and comparing its results to the standard assessment analysis. Utilizing the quadrat-life form as the object- attribute pair, the results derived from the context was analyzed to assess the coral reefs in the study site which consisted of three stations. Data from Station 1 and Station 2 showed the dominance of Acropora digitate and Acropora branching life forms, respectively. Some life forms were absent from both Stations 1 and 2 but all life forms were present in Station 3 with eight life forms having the highest occurrence. Station 3 had the highest diversity of life forms while Station 2 had the highest live coral cover. This study showed how FCA can be used to generate new knowledge from transect data that can be veri ed by traditional coral reef assessment results, a possible complement to standard coral reef assessment analytical tools. FCA approach shines when it deals with large data sets from many different sources, which may pave the way for data-driven ecological assessment analysis studies such as those already being done for agriculture.

With the explosive growth in the world’s population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemented ...

With the explosive growth in the world's population which has little or no corresponding rise in the food production, food insecurity has become eminent, and hence, the need to seek for opportunities to increase food production in order to cater for this population is paramount. The second goal of the Sustainable Development Goals (SDGs) (i.e., ending hunger, achieving food security and improved nutrition, and promoting sustainable agriculture) set by the United Nations (UN) for the year 2030 clearly acknowledged this fact. Improving food production cannot be achieved using the obsolete conventional methods of agriculture by our farmers; hence, this study focuses on developing a model for predicting climatic conditions with a view to reducing their negative impact, and boosting the yield of crop. Temperature, wind, humidity and rainfall were considered as the effect of these factors is more devastating in Nigeria as compared to sun light which is always in abundance. We implemen...

There are various security policy models available to deal with the challenges in multiple domain networks. Applying role based access control (RBAC) is one the widely accepted and currently deployed access control model in multiple domain environments to support mobility constraints of users between domains, inter domain services and its access rights. The objective of this paper is representing various access permissions of multiple domain role based access control model using formal concept analysis (FCA) which is one of the widely used mathematical frameworks for knowledge representation and conceptual analysis. To succeed this objective, we deduce a dyadic formal context from the quadratic security context of inter domain role based access control model. We present the implementation of inter domain role based access control security policy with service access matrix and permission assignment relation. The analysis proves that it is possible to simplify the complex quadratic inter domain security context into simple dyadic context and apply it in the multiple domain networks.

Recent literature reports the growing interests in data analysis using FormalConceptAnalysis (FCA), in which data is represented in the form of object and attribute relations. FCA analyzes and then subsequently visualizes the data based on duality called Galois connection. Attribute exploration is a knowledge acquisition process in FCA, which interactively determines the implications holding between the attributes. The objective of this paper is to demonstrate the attribute exploration to understand the dependencies among the attributes in the data. While performing this process, we add domain experts’ knowledge as background knowledge. We demonstrate the method through experiments on two real world healthcare datasets. The results show that the knowledge acquired through exploration process coupled with domain expert knowledge has better classification accuracy.

Intelligent tutoring systems (ITSs) that personalize instruction to individual learner background and preferences have emerged in K-16 classroom settings all over the world. In mathematics instruction, ITSs may be especially important for tracking mathematical skill development over time. However, recent research has pointed to the importance of text-based measures when solving mathematics word problems, suggesting that in order to accurately model the student it is important to understand how they respond to text characteristics. We investigate the impact of text-based factors (readability and problem topic) on the solving of mathematics story problems using a corpus of N = 3394 students working through an ITS for algebra, Cognitive Tutor Algebra. We leverage recent advances in computerized text-mining to automate fine-grained text analyses of many different word problems. We find that several elements of the text of mathematics word problems matter for performance – including the ...

The technological advancement has provided avenues for complex criminal activities that is hard to comprehend. The ability to detect and understand the type of crimes at various crime scenes will require an efficient visualization scheme. The scheme should enable the detectives or the crime scene inspectors to compare and establish relationships between or among the different elements at various crime scenes. In order to clearly identify the type of crime and the ability to relate different types of crimes at different crime scenes we will need an intelligent information visualization scheme. In this paper we intend to apply Formal Concept Analysis (FCA) which is based on lattice theory to generate a graphical representation model for analyzing various types of crimes at different locations or scenes. Another purpose is to use FCA lattice context diagram for analyzing to classify the nature of criminal activities associated with any given location and draw meaningful conclusions about the area or the location. This approach promises a better visualization and understanding of the relationships among the elements of the crime and the crime scene. It can also be useful for knowledge discovery and knowledge extraction. Furthermore, it can be useful in relating or linking one crime scene to another. An overall benefit is to provide additional resources to augment crime scene processing and documentation.

Computing functional dependencies from a relation is an impor-
tant database topic, with many applications in database management, reverse
engineering and query optimization. Whereas it has been deeply investigated
in those elds, strong links exist with the mathematical framework of For-
mal Concept Analysis. Considering the discovery of functional dependencies,
it is indeed known that a relation can be expressed as the binary relation of a
formal context, whose implications are equivalent to those dependencies. How-
ever, this leads to a new data representation that is quadratic in the number of
objects w.r.t. the original data. Here, we present an alternative avoiding such
a data representation and show how to characterize functional dependencies
using the formalism of pattern structures, an extension of classical FCA to
handle complex data. We also show how another class of dependencies can be
characterized with that framework, namely, degenerated multivalued depen-
dencies. Finally, we discuss and compare the performances of our new approach
in a series of experiments on classical benchmark datasets.

This paper applies forensic data mining to determine the true status of employees and thereafter provide useful evidences for proper administration of administrative rules in a Typical Nigerian Teaching Service. The conventional technique of personnel audit was studied and a new technique for personnel audit was modeled using Artificial Neural Networks and Decision Tree algorithms. Atwolayer classifier architecture was modeled. The outcome of the experiment proved that Radial Basis Function Artificial Neural Network is better than Feed-forward Multilayer Perceptron in modeling of appointment and promotion audit in layer 1 while Logitboost Multiclass Alternating Decision Tree in Layer 2 is best in modeling suspicious appointment audit and abnormal promotion audit among the tested Decision Trees. The evidential rules derived from the decision trees for determining the suspicious appointment and abnormal promotion were also presented.