【摘要】：互聯(lián)網(wǎng)時代來臨,為了避免陷入“數(shù)據(jù)豐富,信息匱乏”的窘迫境地,數(shù)據(jù)挖掘擔負著從海量數(shù)據(jù)中提取有價值的潛在信息并實現(xiàn)數(shù)據(jù)價值的重要使命。數(shù)據(jù)挖掘成為了眾多學者在信息時代研究的熱點之一。聚類是數(shù)據(jù)挖掘中的一個重要研究領域,它作為一種數(shù)據(jù)挖掘工具在諸多領域都有重要的應用。群智能算法是一種新興的啟發(fā)式優(yōu)化算法,根據(jù)生物在生態(tài)系統(tǒng)中以存活、覓食、求偶等行為模擬而來。它具有自學習、分布性、自組織、并行性等特點,能很好地處理傳統(tǒng)計算方法難以解決的一些復雜問題,特別是數(shù)據(jù)分析。群智能算法在處理一些復雜優(yōu)化問題方面具備較大的發(fā)展?jié)摿�。本文詳細論述了�?shù)據(jù)挖掘的基礎知識和幾種常見的群智能算法,分析了聚類算法存在的問題。論文對螢火蟲算法的理論進行了研究和算法改進,并利用改進的算法來解決聚類問題。主要工作如下:(1)針對傳統(tǒng)模糊C均值聚類算法初始聚類中心隨機選取、容易陷入局部最優(yōu)、效率低等問題,本文引入了混沌相關理論,提出了一種混沌初始化方法。然后利用Logistic映射修改螢火蟲位置更新公式,得到較好的聚類效果。實驗結果表明:該算法準確率較高,迭代次數(shù)較少。(2)針對傳統(tǒng)模糊C均值聚類算法全局搜索能力較差、對初始聚類中心選擇較敏感、聚類效果差等缺點,在上一個算法的基礎上提出了一種新的小生境螢火蟲模糊聚類算法。該算法首先采用了隨機性和遍歷性更好的立方映射初始化種群,然后引入隨機慣性權重以修改螢火蟲位置更新公式,以平衡探索和開發(fā)的性能。通過實驗結果可知:該算法提高了聚類質(zhì)量并具有較強魯棒性。(3)針對k-means聚類算法聚類效果差、對初始聚類中心選擇過分依賴、全局搜索能力較差等缺點,提出了一種引入萊維飛行機制的螢火蟲劃分聚類算法。該算法利用基于密度和最大最小距離法來初始化種群,并在螢火蟲個體位置更新公式中引入萊維飛行機制,以避免陷入局部最優(yōu),同時使收斂速度更快,且具有良好的全局搜索能力,最后利用平衡方差評價函數(shù)優(yōu)化目標函數(shù)。實驗結果表明,該算法不僅避免了陷入局部最優(yōu),提高了k-means算法聚類結果質(zhì)量,同時削弱了其對初始值的依賴程度。
[Abstract]:With the advent of the Internet era, in order to avoid falling into the dilemma of "rich data and lack of information", data mining is shouldering the important mission of extracting valuable potential information from massive data and realizing the value of data. Data mining has become one of the hotspots of many scholars in the information age. Clustering is an important research field in data mining. As a data mining tool, it has important applications in many fields. Swarm intelligence algorithm is a new heuristic optimization algorithm, which is simulated by the behavior of survival, foraging, courtship and so on. It has the characteristics of self-learning, distribution, self-organization and parallelism. It can deal with some complicated problems, especially data analysis, which are difficult to solve by traditional computing methods. Swarm intelligence algorithm has great development potential in dealing with some complex optimization problems. In this paper, the basic knowledge of data mining and several common swarm intelligence algorithms are discussed in detail, and the problems of clustering algorithm are analyzed. In this paper, the theory of firefly algorithm is studied and improved, and the improved algorithm is used to solve the clustering problem. The main work is as follows: (1) aiming at the problems of random selection of initial clustering center, easy to fall into local optimum and low efficiency in traditional fuzzy C-means clustering algorithm, chaos correlation theory is introduced and a chaos initialization method is proposed in this paper. Then the Logistic mapping is used to modify the update formula of the firefly position and the clustering effect is obtained. The experimental results show that the algorithm has higher accuracy and fewer iterations. (2) the traditional fuzzy C-means clustering algorithm has poor global search ability, sensitive to the selection of initial clustering centers, and poor clustering effect. Based on the previous algorithm, a new fuzzy clustering algorithm for niche fireflies is proposed. In this algorithm, the population is initialized by cubic mapping with better randomness and ergodicity, and then random inertial weight is introduced to modify the update formula of firefly position to balance the performance of exploration and development. The experimental results show that the algorithm improves the clustering quality and has strong robustness. (3) aiming at the shortcomings of k-means clustering algorithm, such as poor clustering effect, over-dependence on the initial clustering center, poor global search ability, etc. A firefly clustering algorithm based on Levi flight mechanism is proposed. The algorithm initializes the population based on density and maximum and minimum distance, and introduces Levy flight mechanism into the updating formula of individual position of fireflies, so as to avoid falling into local optimum, and at the same time make the convergence speed faster. And it has good global search ability. Finally, the objective function is optimized by the balanced variance evaluation function. Experimental results show that the algorithm not only avoids falling into local optimum, but also improves the quality of clustering results of k-means algorithm and weakens its dependence on initial values.
【學位授予單位】：長沙理工大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP18;TP311.13

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