K Means Clustering With Decision Tree Computer Science Essay

K Means Clustering With Decision Tree Computer Science Essay The K-means clustering data mining algorithm is commonly used to find the clusters due to its simplicity of implementation and fast execution. After applying the K-means clustering algorithm on a dataset, it is difficult for one to interpret and to extract required results from these clusters, until another data mining algorithm is not used. The Decision tree (ID3) is used for the interpretation of the clusters of the K-means algorithm because the ID3 is faster to use, easier to generate understandable rules and simpler to explain. In this research paper we integrate the K-means clustering algorithm with the Decision tree (ID3) algorithm into a one algorithm using intelligent agent, called Learning Intelligent Agent (LIAgent). This LIAgent capable of to do the classification and interpretation of the given dataset. For the visualization of the clusters 2D scattered graphs are drawn. Keywords: Classification, LIAgent, Interpretation, Visualization 1. Introduction The data mining algorithms are applied to discover hidden, new patterns and relations from the complex datasets. The uses of intelligent mobile agents in the data mining algorithms further boost their study. The term intelligent mobile agent is a combination of two different disciplines, the agent is created from Artificial Intelligence and code mobility is defined from the distributed systems. An agent is an object which has independent thread of control and can be initiated. The first step is the agent initialization. The agent will then start to operate and may stop and start again depending upon the environment and the tasks that it tried to accomplish. After the agent finished all the tasks that are required, it will end at its complete state. Table 1 elaborates the different states of an agent [1][2][3][4]. Table 1. States of an agent Name of Step Description Initialize Performs one-time setup activity. Start Start its job or task. Stop Stops its jobs or tasks after saving intermediate results. Complete Performs completion or termination activity. There is link between Artificial Intelligence (AI) and the Intelligent Agents (IA). The data mining is known as Machine Learning in Artificial Intelligence. Machine Learning deals with the development of techniques which allows the computer to learn. It is a method of creating computer programs by the analysis of the datasets. The agents must be able to learn to do classification, clustering and prediction using learning algorithms [5][6][7][8]. The remainder of this paper is organized as followos: Section 2 reviews the relevant data mining algoritms, namely the K-means clustering and the Decision tree (ID3). Section 3 is about the methodology; a hybrid integration of the data mining algorithms. In section 4 we discuss the results and dicussion. Finally section 5 presents the conclusion. 2. Overview of Data Mining Algorithms The K-means clustering data mining algorithm is used for the classification of a dataset by producing the clusters of that dataset. The K-means clustering algorithm is a kind of unsupervised learning of machine learning. The decision tree (ID3) data mining algorithm is used to interpret these clusters by producing the decision rules in if-then-else form. The decision tree (ID3) algorithm is a type of supervised learning of machine learning. Both of these algorithms are combined in one algorithm through intelligent agents, called Learning Intelligent Agent (LIAgent). In this section we will discuss both of these algorithms. 2.1. K-means clustering Algorithm The following steps explain the K-means clustering algorithm: Step 1: Enter the number of clusters and number of iterations, which are the required and basic inputs of the K-means clustering algorithm. Step 2: Compute the initial centroids by using the Range Method shown in equations 1 and 2. (1) (2) The initial centroid is C(ci, cj).Where: max X, max Y, min X and min Y represent maximum and minimum values of X and Y attributes respectively. k represents the number of clusters and i, j and n vary from 1 to k where k is an integer. In this way, we can calculate the initial centroids; this will be the starting point of the algorithm. The value (maxX minX) will provide the range of X attribute, similarly the value (maxY minY) will give the range of Y attribute. The value of n varies from 1 to k. The number of iterations should be small otherwise the time and space complexity will be very high and the value of initial centroids will also become very high and may be out of the range in the given dataset. This is a major drawback of the K-means clustering algorithm. Step 3: Calculate the distance using Euclideans distance formula in equation 3. On the basis of the distances, generate the partition by assigning each sample to the closest cluster. Euclidean Distance Formula: (3) Where d(xi, xj) is the distance between xi and xj. xi and xj are the attributes of a given object, where i and j vary from 1 to N where N is total number of attributes of a given object. i,j and N are integers. Step 4: Compute new cluster centers as centroids of the clusters, again compute the distances and generate the partition. Repeat this until the cluster memberships stabilizes [9][10]. The strengths and weaknesses of the K-means clustering algorithm are discussed in table 2. Table 2. Strengths and Weakness of the K-means clustering Algorithm Strengths Weaknesses Time complexity is O(nkl). Linear time complexity in the size of the dataset. It is easy to implement, it has the drawback of depending on the initial centre provided. Space complexity is O(k + n). If a distance measure does not exist, especially in multidimensional spaces, first define the distance, which is not always easy. It is an order-independent algorithm. It generates same partition of data irrespective of order of samples. The Results obtained from this clustering algorithm can be interpreted in different ways. Not applicable All clustering techniques do not address all the requirements adequately and concurrently. The following are areas but not limited to where the K-means clustering algorithm can be applied: Marketing: Finding groups of customers with similar behavior given large database of customer containing their profiles and past records. Biology: Classification of plants and animals given their features. Libraries: Book ordering. Insurance: Identifying groups of motor insurance policy holders with a high average claim cost; identifying frauds. City-planning: Identifying groups of houses according to their house type, value and geographically location. Earthquake studies: Clustering observed earthquake epicenters to identify dangerous zones. WWW: Document classification; clustering web log data to discover groups of similar access patterns. Medical Sciences: Classification of medicines; patient records according to their doses etc. [11][12]. 2.2. Decision Tree (ID3) Algorithm The decision tree (ID3) produces the decision rules as an output. The decision rules obtained from ID3 are in the form of if-then-else, which can be use for the decision support systems, classification and prediction. The decision rules are helpful to form an accurate, balanced picture of the risks and rewards that can result from a particular choice. The function of the decision tree (ID3) is shown in the figure 1. Figure 1. The Function of Decision Tree (ID3) algorithm The cluster is the input data for the decision tree (ID3) algorithm, which produces the decision rules for the cluster. The following steps explain the Decision Tree (ID3) algorithm: Step 1: Let S is a training set. If all instances in S are positive, then create YES node and halt. If all instances in S are negative, create a NO node and halt. Otherwise select a feature F with values v1,,vn and create a decision node. Step 2: Partition the training instances in S into subsets S1, S2, , Sn according to the values of V. Step 3: Apply the algorithm recursively to each of the sets Si [13][14]. Table 3 shows the strengths and weaknesses of ID3 algorithm. Table 3. Strengths and Weaknesses of Decision Tree (ID3) Algorithm Strengths Weaknesses It generates understandable rules. It is less appropriate for a continuous attribute. It performs classification without requiring much computation. It does not perform better in problems with many class and small number of training examples. It is suitable to handle both continuous and categorical variables. The growing of a decision tree is expensive in terms of computation because it sorts each node before finding the best split. It provides an indication for prediction or classification. It is suitable for a single field and does not treat well on non-rectangular regions. 3. Methodology We combine two different data mining algorithms namely the K-means clustering and Decision tree (ID3) into a one algorithm using intelligent agent called Learning Intelligent Agent (LIAgent). The Learning Intelligent Agent (LIAgent) is capable of clustering and interpretation of the given dataset. The clusters can also be visualized by using 2D scattered graphs. The architecture of this agent system is shown in figure 2. Figure 2. The Architecture of LIAgent System The LIAgent is a combination of two data mining algorithms, the one is the K-means clustering algorithm and the second is the Decision tree (ID3) algorithm. The K-means clustering algorithm produces the clusters of the given dataset which is the classification of that dataset and the Decision tree (ID3) will produce the decision rules for each cluster which are useful for the interpretation of these clusters. The user can access both the clusters and the decision rules from the LIAgent. This LIAgent is used for the classification and the interpretation of the given dataset. The clusters of the LIAgent are further used for visualization using 2D scattered graphs. Decision tree (ID3) is faster to use, easier to generate understandable rules and simpler to explain since any decision that is made can be understood by viewing path of decision. They also help to form an accurate, balanced picture of the risks and rewards that can result from a particular choice. The decision rules are obta ined in the form of if-then-else, which can be used for the decision support systems, classification and prediction. A medical dataset Diabetes is used in this research paper. This is a dataset/testbed of 790 records. The data of Diabetes dataset is pre-processed, called the data standardization. The interval scaled data is properly cleansed. The attributes of the dataset/testbed Diabetes are: Number of times pregnant (NTP)(min. age = 21, max. age = 81) Plasma glucose concentration a 2 hours in an oral glucose tolerance test (PGC) Diastolic blood pressure (mm Hg) (DBP) Triceps skin fold thickness (mm) (TSFT) 2-Hour serum insulin (m U/ml) (2HSHI) Body mass index (weight in kg/(height in m)^2) (BMI) Diabetes pedigree function (DPF) Age Class (whether diabetes is cat 1 or cat 2) [15]. We create the four vertical partitions of the dataset Diabetes, by selecting the proper number of attributes. This is illustrated in tables 4 to 7. Table 4. 1st Vertically partition of Diabetes Dataset NTP DPF Class 4 0.627 -ive 2 0.351 +ive 2 2.288 -ive Table 5. 2nd Vertically partition of Diabetes Dataset DBP AGE Class 72 50 -ive 66 31 +ive 64 33 -ive Table 6. 3rd Vertically partition of Diabetes Dataset TSFT BMI Class 35 33.6 -ive 29 28.1 +ive 0 43.1 -ive Table 7. 4th Vertically partition of Diabetes Dataset PGC 2HIS Class 148 0 -ive 85 94 +ive 185 168 -ive Each partitioned table is a dataset of 790 records; only 3 records are exemplary shown in each table. For the LIAgent, the number of clusters k is 4 and the number of iterations n in each case is 50 i.e. value of k =4 and value of n=50. The decision rules of each clusters is obtained. For the visualization of the results of these clusters, 2D scattered graphs are also drawn. 4. Results and Discussion The results of the LIAgent are discussed in this section. The LIAgent produces the two outputs, namely, the clusters and the decision rules for the given dataset. The total sixteen clusters are obtained for all four partitions, four clusters per partition. Not all the clusters are good for the classification, only the required and useful clusters are discussed for further information. The sixteen decision rules are also generated by LIAgent. We are presenting three decision rules of three different clusters. The number of decision rules varies from cluster to cluster; it depends upon the number of records in the cluster. The Decision Rules of the 4th partition of the dataset Diabetes: Rule: 1 if PGC = 165 then Class = Cat2 else Rule: 2 if PGC = 153 then Class = Cat2 else Rule: 3 if PGC = 157 then Class = Cat2 else Rule: 4 if PGC = 139 then Class = Cat2 else Rule: 5 if HIS = 545 then Class = Cat2 else Rule: 6 if HIS = 744 then Class = Cat2 else Class = Cat1 Only six decision rules are for the 4th partition of the dataset. It is easy for any one to take the decision and interpret the results of this cluster. The Decision Rules of the 1st partition of the dataset Diabetes: Rule: 1 if DPF = 1.32 then Class = Cat1 else Rule: 2 if DPF = 2.29 then Class = Cat1 else Rule: 3 if NTP = 2 then Class = Cat2 else Rule: 4 if DPF = 2.42 then Class = Cat1 else Rule: 5 if DPF = 2.14 then Class = Cat1 else Rule: 6 if DPF = 1.39 then Class = Cat1 else Rule: 7 if DPF = 1.29 then Class = Cat1 else Rule: 8 if DPF = 1.26 then Class = Cat1 else Class = Cat2 The eight decision rules are for the 1st partition of the dataset. The interpretation of the cluster is easy through the decision rules and it also helps to take the decision. The Decision Rules of the 3rd partition of the dataset Diabetes: Rule: 1 if BMI = 29.9 then Class = Cat1 else Rule: 2 if BMI = 32.9 then Class = Cat1 else Rule: 3 if TSFK = 23 then Rule: 4 if BMI = 25.5 then Class = Cat1 else Rule: 5 if BMI = 30.1 then Class = Cat1 else Rule: 6 if BMI = 28.4 then Class = Cat1 else Class = Cat2 else Rule: 7 if BMI = 22.9 then Class = Cat1 else Rule: 8 if BMI = 27.6 then Class = Cat1 else Rule: 9 if BMI = 29.7 then Class = Cat1 else Rule: 10 if BMI = 27.1 then Class = Cat1 else Rule: 11 if BMI = 25.8 then Class = Cat1 else Rule: 12 if BMI = 28.9 then Class = Cat1 else Rule: 13 if BMI = 23.4 then Class = Cat1 else Rule: 14 if BMI = 30.5 then Rule: 15 if TSFK = 18 then Class = Cat2 else Class = Cat1 else Rule: 16 if BMI = 26.6 then Rule: 17 if TSFK = 18 then Class = Cat2 else Class = Cat1 else Rule: 18 if BMI = 32 then Rule: 19 if TSFK = 15 then Class = Cat2 else Class = Cat1 else Rule: 20 if BMI = 31.6 then Class = Cat2 , Cat1 else Class = Cat2 The twenty decision rules are for the 3rd partition of the dataset. The number of rules for this cluster is higher than the other two clusters discussed. The visualization is important tool which provides the better understanding of the data and illustrates the relationship among the attributes of the data. For the visualization of the clusters 2D scattered graphs are drawn for all the clusters. We are presenting the four 2D scattered graphs of four different clusters of different partitions. Figure 3. 2D Scattered Graph between NTP and DPF attributes of Diabetes dataset The distance between NTP and DPF attributes of Diabetes dataset varies at the beginning of the graph but after some interval the distance becomes constant. Figure 4. 2D Scattered Graph between DBP and AGE attributes of Diabetes dataset There is a variable distance between DBP and AGE attributes of the dataset. It remains variable throughout this graph. Figure 5. 2D Scattered Graph between TSFT and BMI attributes of Diabetes dataset The graph shows almost constant distance between TSFT and BMI attributes of the dataset. It remains constant throughout the graph. Figure 6. 2D Scattered Graph between PGC and 2HIS attributes of Diabetes dataset There is a variable distance between PGC and 2HIS attributes of the dataset. But in the middle of this graph there is some constant distance between these attributes. The structure of this graph is similar to the graph of figure 5. 5. Conclusion It is not simple for all the users that they can interpret and extract the required results from these clusters, until some other data mining algorithms or other tools are not used. In this research paper we have tried to address the issue by integrating the K-means clustering algorithm with the Decision tree (ID3) algorithm. The choice of the ID3 is due to the decision rules in the form of if-then-else as an output, which are easy to understand and help to take the decision. It is a hybrid combination of supervised and unsupervised machine learning, using intelligent agent, called a LIAgent. The LIAgent is helpful in the classification and prediction of the given dataset. Furthermore, 2D scattered graphs of the clusters are drawn for the visualization.

Ernest Hemingway Essay -- essays research papers

  Ã‚  Ã‚  Ã‚  Ã‚  Ernest Hemingway was one of the greatest writers of the century. He was born at the close of the old century but was able to see the Disorders of the new century. Hemingway was marvelous in bringing about his pictorial effects for his readers even in his drunken state. Hemingway was skilled in the way he presented the â€Å"real† and â€Å"concrete† to be the first essentials in his writing. He put life back on the page so that we could see the grim reality of the truth. Hemingway’s style brought minute details to the surface so that the readers would understand his meanings. In the stories that I have chosen the critics have analyzed the story. In this paper I intend to prove that Ernest Hemingways writing in â€Å"Soldiers Home† and â€Å"Hills Like White Elephants† influenced American writing styles through Symbols, Themes and writing techniques.   Ã‚  Ã‚  Ã‚  Ã‚  In several of Hemingway’s short stories, he uses one or more animals as symbols around which the story revolves. As central symbols, Hemingway’s animals are the manifestations of the psychological states and emotional desire of the main characters in the stories. He uses the symbols to enable the reader to comprehend the often not stated psychological forces that motivates them. Hemingway’s use of symbolism is a contribution to the richness of his characters. It provides the reader with a vehicle through which they can associate. Without them the stories would lose much of their color and clarity. In the short story â€Å"Soldiers Home,† Majorie Smelstor said â€Å" the title of this story suggests a familiar American landmark and symbol, The soldier’s home, a place for retired military to live and relive their war experiencesâ€Å" In this tale, however, the soldier’s home is neither a haven for ex-soldiers nor an envir onment for reminiscing† (MaGill 2170). Another symbol is the lies that Kerb lives with every day. Marjorie Smelstor said â€Å"For the townspeople do not want to hear the truth about the atrocities of battle, preferring, instead, lies about the heroics of war† (MaGill 2170). Marjorie Smelstor said In the end of the story â€Å"Mrs. Kerb’s reasserts her maternal role, reminding her son that she held him next to her heart when he was a tiny baby, Reducing kerb’s to the juvenile lie:† â€Å"I know, mummy†¦ I’ll try and be a good boy for you† (MaGill 2171).   Ã‚  Ã‚  Ã‚  Ã‚  ... ...ise† is a cleansing drink. It has a seed that aids in expelling gas from the alimentary canal to relieve colic. Colic is a paroxysm of abdominal pain localized in a hollow organ. The American orders this drink for him and the girl. â€Å"This clearly shows you that the conflict and lack of communication between the two of them† (Passey 33). The answer is still unresolved and the girl must choose between the man and the baby. At the end of the story the reader is not sure of the girl's decision. Hemingway left the end of the story unresolved.   Ã‚  Ã‚  Ã‚  Ã‚     Ã‚  Ã‚  Ã‚  Ã‚  In his life time Ernest Hemingway enjoyed tremendous success as a writer. His ability to describe a story in detail was his best talent. On the two stories, Ernest showed his unique ability of story telling. Hemingway was very clever in using Symbols to bring his characters to life. Hemingway wrote many of war stories but was never a soldier. He just had the talent of looking at life and writing it down in a symbolic way. Most all the critics come up with the conclusion that Hemingway was truly talented. He influenced other American writers with his style, symbols, themes and technique.

Development of Axum and Meroe in Northeastern Africa Essay

66. Comparative Analysis: Compare the development of Axum and Meroe in northeastern Africa with the development of the Maya and of Teotihuacan in Mesoamerica. In both the development of Axum and Meroà « in northeastern Africa possessed the ores and fuels needed to produce iron on a large scale. They traded along the Nile River to Egypt, and their goods such as gold and ivory reached ports all along the Mediterranean Sea, Red Sea, Southern Africa, and quite possibly as far as India and China. Axum was a very powerful state. It controlled a huge number of ports, such as Adulis along the Red Sea coast, and it participated in the commerce of the Indian Ocean, where its export goods included ivory, slaves, and crystal. They also traded with Alexandrian Egypt, and eventually with Rome, Byzantium, and India. Teotihuacan was a powerful political, military, economic and cultural center that influenced the whole of Mesoamerica. Teotihuacan is one of the world’s oldest and most impressive archaeological sites. The wealth of Teotihuacan was based largely on the amount they had on the trade of obsidian, a coarse green glass occurring in volcanic rock. In common with the other Mesoamerican civilizations, the Maya had measured the length of the solar year to a high degree of accuracy. They produced extremely accurate astronomical observations; their charts of the movements of the moon and planets were used to predict eclipses and other celestial events such the time between conjunctions of Venus. The ancient Maya had diverse and sophisticated methods of food production. It was formerly believed that shifting cultivation agriculture provided most of their food but it is now thought that permanent raised fields, terracing, forest gardens, managed fallows, and wild harvesting were also crucial to supporting the large populations of the Classic period in some areas. 67. Historical Analysis: â€Å"While long-distance trade across Afro-Eurasia predates 500 C.E., it grew in importance between 500 and 1500 C.E.† What key developments make this statement true? In between 500 and 1500 C.E Afro-Eurasia’s trade increased and altered consumption patterns and encouraged peoples across the zone to specialize in producing particular goods for market rather than being self-sufficient. They followed religions such as Buddhism and Islam and it spread along the trade routes. During this period, there were a lot of epidemic diseases, and they spread along the trade routes. Some regions developed stronger states in part because of increased commerce along the trade networks. These regions were East Africa, West Africa, and Southeast Asia. There were also a lot of technological advances during this time. This included the development of larger ships and the magnetic compass in China. This also helped increase trade.

Sopwith Camel - Top World War I Fighter

The iconic Allied aircraft of World War I (1914-1918), the Sopwith Camel entered service in mid-1917 and helped the reclaim the skies over the Western Front from the Deutsche Luftstreitkrà ¤fte (Imperial German Air Service). An evolution of an earlier Sopwith fighter, the Camel mounted twin .30 cal. Vickers machine guns and was capable of around 113 mph in level flight. A difficult aircraft for novices to fly, its idiosyncrasies made it one of the most maneuverable aircraft on either side in the hands of an experienced pilot. These characteristics helped make it the most lethal Allied fighter of the war.   Design Development: Designed by Herbert Smith, the Sopwith Camel was a follow-on aircraft to the Sopwith Pup. A largely successful aircraft, the Pup had become outclassed by new German fighters, such as the Albatros D.III in early 1917. The result was a period known as Bloody April which saw Allied squadrons sustain heavy losses as their Pups, Nieuport 17s, and older aircraft were downed in large numbers by the Germans. Initially known as the Big Pup the Camel was initially powered by a 110 hp Clerget 9Z engine and featured a visually heavier fuselage than its predecessor. This was largely composed of fabric over a wooden frame with plywood panels around the cockpit and an aluminum engine cowling. Structurally, the aircraft featured a straight upper wing with a very pronounced dihedral on the lower wing. The new Camel was the first British fighter to utilize twin .30 cal. Vickers machine guns firing through the propeller. The metal fairing over the guns breeches, which was intended to keep the weapons from freezing at higher altitudes, formed a hump which led to the aircrafts name. A nickname, the term Camel was never officially adopted by the Royal Flying Corps. Handling Within in the fuselage, the engine, pilot, guns, and fuel were grouped within the first seven feet of the aircraft. This forward center of gravity, coupled with the significant gyroscopic effect of the rotary engine, made the aircraft difficult to fly particularly for novice aviators. This was a significant change from earlier Sopwith aircraft which had been considered fairly easy to fly. To facilitate the transition to the aircraft, two-seat trainer variants of the Camel were produced. The Sopwith Camel was known to climb in a left turn and dive in a right turn. Mishandling the aircraft often could lead to a dangerous spin. Also, the aircraft was known to be consistently tail heavy in level flight at low altitudes and required steady forward pressure on the control stick to maintain a steady altitude. While these handling characteristics challenged pilots, they also made the Camel extremely maneuverable and lethal in combat when flown by a skilled pilot such as Canadian ace William George Barker. Sopwith Camel - Specifications: General Length: 18 ft. 9 in.Wingspan: 26 ft. 11 in.Height: 8 ft. 6 in.Wing Area: 231 sq. ft.Empty Weight: 930 lbs.Crew: 1 Performance Power Plant: 1 Ãâ€" Clerget 9B 9-cylinder Rotary engine, 130 hpRange: 300 milesMax Speed: 113 mphCeiling: 21,000 ft. Armament Guns: 2 Ãâ€" .30 cal. Vickers machine guns Production Flying for the first time on December 22, 1916, with Sopwith test pilot Harry Hawker at the controls, the prototype Camel impressed and the design was further developed. Accepted into service by the Royal Flying Corps as the Sopwith Camel F.1, the majority of the production aircraft were powered by 130 hp Clerget 9B engine. The first order for the aircraft was issued by the War Office in May 1917. Subsequent orders saw the production run total around 5,490 aircraft. During its production, the Camel was fitted with a variety of engines including the 140 hp Clerget 9Bf, 110 hp Le Rhone 9J, 100 hp Gnome Monosoupape 9B-2, and 150 hp Bentley BR1. Operational History Arriving at the front in June 1917, the Camel debuted with No.4 Squadron Royal Naval Air Service and quickly showed its superiority over the best German fighters, including both the Albatros D.III and D.V. The aircraft next appeared with No. 70 Squadron RFC and ultimately would be flown by over fifty RFC squadrons. An agile dog fighter, the Camel, along with the Royal Aircraft Factory S.E.5a and the French SPAD S.XIII, played a key role in reclaiming the skies over the Western Front for the Allies. In addition to British use, 143 Camels were purchased by the American Expeditionary Force and flown by several of its squadrons. The aircraft was also used by Belgian and Greek units. Other Uses In addition to service ashore, a navalized version of the Camel, the 2F.1, was developed for use by the Royal Navy. This aircraft featured a slightly shorter wingspan and replaced one of the Vickers machine guns with a .30 cal Lewis gun firing over the top wing. Experiments were also conducted in 1918 using 2F.1s as parasite fighters carried by British airships. Camels were also used as night fighters though with some modifications. As the muzzle-flash from the twin Vickers wrecked the pilots night vision, the Camel Comic night fighter possessed twin Lewis guns, firing incendiary ammunition, mounted on the upper wing. Flying against German Gotha bombers, the Comics cockpit was situated farther aft than the typical Camel to allow the pilot to more easily reload the Lewis guns. Later Service By mid-1918, the Camel was slowly becoming out-classed by new fighters arriving on the Western Front. Though it remained in frontline service due to development issues with its replacement, the Sopwith Snipe, the Camel was increasingly used in a ground support role. During the German Spring Offensives flights of Camels attacked German troops with devastating effect. On these missions the aircraft typically strafed enemy positions and dropped 25-lb. Cooper bombs. Replaced by the Snipe at the conclusion of World War I the Camel downed a minimum of 1,294 enemy aircraft making it the deadliest Allied fighter of the war. Following the war, the aircraft was retained by several nations including the United States, Poland, Belgium, and Greece. In the years after the war, the Camel became entrenched in pop culture through a variety of films and books about the air war over Europe. More recently, the Camel commonly appeared in the popular Peanuts cartoons as the favored plane of Snoopy during his imaginary battles with the Red Baron.