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The academic community places a high value on the originality and authenticity of research findings. As such, identifying plagiarism in scholarly work is of utmost importance. In recent years, hierarchical clustering has emerged as a powerful tool for detecting plagiarism in a wide range of text-based research. In this paper, we explore the application of hierarchical clustering to the analysis of alchemical texts, intending to identify instances of plagiarism within this notoriously complex and esoteric body of literature.
Alchemical texts pose unique challenges to plagiarism detection, as they often contain obscure symbolism and metaphorical language that makes it difficult to identify textual similarities. Nonetheless, scholars have long recognized the importance of analyzing alchemical texts for insights into the history and philosophy of science. The works of Conrad (1992), Young and Krippner (2001), Ramos et al. (2007), Ali and Khan (2013), and Dhane and Kadam (2019) have demonstrated the efficacy of hierarchical clustering in analyzing alchemical texts. By applying this method, researchers have been able to gain new insights into the language, symbolism, and themes of alchemy, shedding light on this ancient and enigmatic practice.
However, to date, little research has been done on the use of hierarchical clustering for identifying plagiarism within alchemical texts. This paper seeks to fill this gap in the literature. By analyzing a selected sample of alchemical texts using hierarchical clustering, we aim to demonstrate the usefulness of this method for identifying instances of plagiarism within these complex and often mysterious works. Through this study, the author hopes to contribute to a deeper understanding of alchemy and to advance the field of plagiarism detection.
Literature Review
Alchemy has a rich and complex history, spanning centuries and civilizations. The text "The History of Alchemy" by M. E. Weeks (2017) provides a comprehensive overview of the history of alchemy from its earliest origins to its modern-day legacy. According to Weeks, the practice of alchemy was not limited to the transmutation of metals but also included the pursuit of spiritual and philosophical enlightenment. M. L. von Franz (2005), M. P. Stevens (2013), and A. McLean (2005) explores various aspects of alchemy, including its symbolism and psychology, its place in the Western esoteric tradition, and its historical impact on art and literature around the world. It has been studied extensively by scholars across a range of disciplines, including history, philosophy, psychology, and literature. One of the challenges that scholars face in studying alchemy is the fact that many of the texts are written in esoteric and allegorical language, making them difficult to interpret and conceptualize.
One issue that has arisen in the study of alchemy is plagiarism. Borowitz (2019) notes that plagiarism has been a problem in academia for centuries, and alchemical texts are no exception. Due to the secrecy and mysticism surrounding alchemy, it was not uncommon for alchemists to borrow heavily from one another's work without giving proper credit. This can make it difficult for scholars to accurately trace the development of alchemical ideas and practices over time.
A powerful tool for detecting plagiarism in alchemical texts is hierarchical clustering. The first scholar to use clustering methods to analyze alchemical texts was Conrad (1992). Using hierarchical clustering, he identified groups of texts that shared similar language and themes among 19th-century alchemical texts. Young and Krippner (2001) built on Conrad's work by applying latent semantic analysis to cluster alchemical texts. They found that the use of clustering methods allowed them to identify patterns and connections between texts that were not immediately apparent.
Ramos et al. (2007) also used hierarchical clustering to analyze alchemical texts. They applied a clustering algorithm to a corpus of alchemical texts and found that it was able to group texts based on their language and themes. Ali and Khan (2013) used a similar approach, applying hierarchical clustering to a dataset of Persian alchemical texts. They found that clustering was able to reveal previously unnoticed connections between texts and allowed them to identify instances of plagiarism.
Dhane and Kadam (2019) also used hierarchical clustering to identify similar works within a corpus of alchemical texts. They found that clustering was able to group texts based on their themes and symbolism, which allowed them to identify works that shared common features.
In addition to its use in identifying plagiarism, hierarchical clustering has also been used to gain a deeper understanding of the structure and language of alchemical texts. Chakraborty and Chaudhuri (2016) provide an introduction to hierarchical clustering and its applications, including its use in text analysis. Nainar and Sinha (2020) review different approaches to hierarchical clustering and their applications, including the use of clustering in text mining. Pham et al. (2020) discuss the use of hierarchical clustering in Vietnamese text summarization, highlighting its ability to group similar texts.
In the analysis of alchemical texts, hierarchical clustering has shed new light on this enigmatic practice. Researchers have been able to gain a deeper understanding of alchemy's language, symbolism, and themes by identifying patterns and relationships within the texts. Additionally, clustering methods have allowed scholars to identify instances of plagiarism, which is essential for accurately tracing alchemical ideas over time.
Hierarchical clustering is not limited to text analysis and plagiarism use cases. Li et al. (2010) used hierarchical clustering to group music genres based on similarities in their audio features. Lusher et al. (2013), for analysis and visualization of patterns in social networks and Diedrichsen et al. (2011) utilized clustering to group regions of the brain and to identify patterns of activation that are associated with different cognitive processes.
Overall, the literature suggests that hierarchical clustering is a valuable tool for the analysis of alchemical texts. Its ability to group texts based on their language, themes, and symbolism makes it a useful method for identifying patterns and relationships within the texts, as well as instances of plagiarism. Scholars can continue to gain new insights into this fascinating and complicated subject by applying hierarchical clustering to alchemical texts.
Methodology
The purpose of this study is to determine whether hierarchical clustering can be used to spot plagiarism in alchemical texts. I intend to utilize a dataset of alchemical texts that is considered by experts to have credible authors, as well as some texts that have been criticized for plagiarism in order to accomplish this objective.
The dataset will consist of a collection of alchemical texts obtained from various sources, including online repositories and academic libraries. Manuel preprocessing of the data will be employed to ensure consistency and extract the relevant features from the texts. I will then apply hierarchical clustering algorithms to group the texts based on their similarity in terms of these features.
To evaluate the performance of our approach, I will use a set of metrics commonly used in clustering analysis, such as the silhouette coefficient, the Davies-Bouldin index, and the Rand index. I will also compare our results with those obtained using other clustering methods and traditional plagiarism detection techniques, such as n-gram analysis. Rasmussen et al. (2002); Goodfellow (2016); LeCun (2015).
Additionally, I plan to inspect a sample of the clustered texts manually to determine the effectiveness of the hierarchical clustering approach in identifying plagiarism.
As a result of this study, more effective plagiarism detection methods can be developed for historical texts, such as alchemical texts, so that researchers and historians can analyze how ideas and knowledge evolved in the field of alchemy over time.
Results (Future Work)
The intended approach is to use hierarchical clustering to identify patterns of plagiarism in alchemical texts. The results of this analysis will be presented in the form of dendrograms, which are tree-like diagrams that illustrate the relationships between different clusters of text. The dendrograms will be annotated to highlight the specific areas of the text that are suspected of being plagiarized. Additionally, the identified patterns of plagiarism will be compared to known examples of plagiarism in alchemical literature to further validate the method.
It is anticipated that this approach will reveal new insights into the practice of plagiarism in alchemical literature and contribute to a better understanding of the evolution of alchemical thought.
Alchemical texts pose unique challenges to plagiarism detection, as they often contain obscure symbolism and metaphorical language that makes it difficult to identify textual similarities. Nonetheless, scholars have long recognized the importance of analyzing alchemical texts for insights into the history and philosophy of science. The works of Conrad (1992), Young and Krippner (2001), Ramos et al. (2007), Ali and Khan (2013), and Dhane and Kadam (2019) have demonstrated the efficacy of hierarchical clustering in analyzing alchemical texts. By applying this method, researchers have been able to gain new insights into the language, symbolism, and themes of alchemy, shedding light on this ancient and enigmatic practice.
However, to date, little research has been done on the use of hierarchical clustering for identifying plagiarism within alchemical texts. This paper seeks to fill this gap in the literature. By analyzing a selected sample of alchemical texts using hierarchical clustering, we aim to demonstrate the usefulness of this method for identifying instances of plagiarism within these complex and often mysterious works. Through this study, the author hopes to contribute to a deeper understanding of alchemy and to advance the field of plagiarism detection.
Literature Review
Alchemy has a rich and complex history, spanning centuries and civilizations. The text "The History of Alchemy" by M. E. Weeks (2017) provides a comprehensive overview of the history of alchemy from its earliest origins to its modern-day legacy. According to Weeks, the practice of alchemy was not limited to the transmutation of metals but also included the pursuit of spiritual and philosophical enlightenment. M. L. von Franz (2005), M. P. Stevens (2013), and A. McLean (2005) explores various aspects of alchemy, including its symbolism and psychology, its place in the Western esoteric tradition, and its historical impact on art and literature around the world. It has been studied extensively by scholars across a range of disciplines, including history, philosophy, psychology, and literature. One of the challenges that scholars face in studying alchemy is the fact that many of the texts are written in esoteric and allegorical language, making them difficult to interpret and conceptualize.
One issue that has arisen in the study of alchemy is plagiarism. Borowitz (2019) notes that plagiarism has been a problem in academia for centuries, and alchemical texts are no exception. Due to the secrecy and mysticism surrounding alchemy, it was not uncommon for alchemists to borrow heavily from one another's work without giving proper credit. This can make it difficult for scholars to accurately trace the development of alchemical ideas and practices over time.
A powerful tool for detecting plagiarism in alchemical texts is hierarchical clustering. The first scholar to use clustering methods to analyze alchemical texts was Conrad (1992). Using hierarchical clustering, he identified groups of texts that shared similar language and themes among 19th-century alchemical texts. Young and Krippner (2001) built on Conrad's work by applying latent semantic analysis to cluster alchemical texts. They found that the use of clustering methods allowed them to identify patterns and connections between texts that were not immediately apparent.
Ramos et al. (2007) also used hierarchical clustering to analyze alchemical texts. They applied a clustering algorithm to a corpus of alchemical texts and found that it was able to group texts based on their language and themes. Ali and Khan (2013) used a similar approach, applying hierarchical clustering to a dataset of Persian alchemical texts. They found that clustering was able to reveal previously unnoticed connections between texts and allowed them to identify instances of plagiarism.
Dhane and Kadam (2019) also used hierarchical clustering to identify similar works within a corpus of alchemical texts. They found that clustering was able to group texts based on their themes and symbolism, which allowed them to identify works that shared common features.
In addition to its use in identifying plagiarism, hierarchical clustering has also been used to gain a deeper understanding of the structure and language of alchemical texts. Chakraborty and Chaudhuri (2016) provide an introduction to hierarchical clustering and its applications, including its use in text analysis. Nainar and Sinha (2020) review different approaches to hierarchical clustering and their applications, including the use of clustering in text mining. Pham et al. (2020) discuss the use of hierarchical clustering in Vietnamese text summarization, highlighting its ability to group similar texts.
In the analysis of alchemical texts, hierarchical clustering has shed new light on this enigmatic practice. Researchers have been able to gain a deeper understanding of alchemy's language, symbolism, and themes by identifying patterns and relationships within the texts. Additionally, clustering methods have allowed scholars to identify instances of plagiarism, which is essential for accurately tracing alchemical ideas over time.
Hierarchical clustering is not limited to text analysis and plagiarism use cases. Li et al. (2010) used hierarchical clustering to group music genres based on similarities in their audio features. Lusher et al. (2013), for analysis and visualization of patterns in social networks and Diedrichsen et al. (2011) utilized clustering to group regions of the brain and to identify patterns of activation that are associated with different cognitive processes.
Overall, the literature suggests that hierarchical clustering is a valuable tool for the analysis of alchemical texts. Its ability to group texts based on their language, themes, and symbolism makes it a useful method for identifying patterns and relationships within the texts, as well as instances of plagiarism. Scholars can continue to gain new insights into this fascinating and complicated subject by applying hierarchical clustering to alchemical texts.
Methodology
The purpose of this study is to determine whether hierarchical clustering can be used to spot plagiarism in alchemical texts. I intend to utilize a dataset of alchemical texts that is considered by experts to have credible authors, as well as some texts that have been criticized for plagiarism in order to accomplish this objective.
The dataset will consist of a collection of alchemical texts obtained from various sources, including online repositories and academic libraries. Manuel preprocessing of the data will be employed to ensure consistency and extract the relevant features from the texts. I will then apply hierarchical clustering algorithms to group the texts based on their similarity in terms of these features.
To evaluate the performance of our approach, I will use a set of metrics commonly used in clustering analysis, such as the silhouette coefficient, the Davies-Bouldin index, and the Rand index. I will also compare our results with those obtained using other clustering methods and traditional plagiarism detection techniques, such as n-gram analysis. Rasmussen et al. (2002); Goodfellow (2016); LeCun (2015).
Additionally, I plan to inspect a sample of the clustered texts manually to determine the effectiveness of the hierarchical clustering approach in identifying plagiarism.
As a result of this study, more effective plagiarism detection methods can be developed for historical texts, such as alchemical texts, so that researchers and historians can analyze how ideas and knowledge evolved in the field of alchemy over time.
Results (Future Work)
The intended approach is to use hierarchical clustering to identify patterns of plagiarism in alchemical texts. The results of this analysis will be presented in the form of dendrograms, which are tree-like diagrams that illustrate the relationships between different clusters of text. The dendrograms will be annotated to highlight the specific areas of the text that are suspected of being plagiarized. Additionally, the identified patterns of plagiarism will be compared to known examples of plagiarism in alchemical literature to further validate the method.
It is anticipated that this approach will reveal new insights into the practice of plagiarism in alchemical literature and contribute to a better understanding of the evolution of alchemical thought.
